We are in the Wild West of AI writing.

Everyone is using AI to move faster. Yet workplace norms are still blurry. We have tools, prompts, and output but very little shared etiquette.

Should you use AI to help write:

• the team's weekly motivational message in Slack?

• your LinkedIn post?

• feedback to a teammate after a rough project?

According to Fortune, AI's favorability rating is 26%. That alone should make us think twice before using AI to write communication that shapes how other people perceive us.

How we use AI in the workplace matters more than most people realize. Words carry emotion. AI-written words do too. Is the emotion these words convey helping or damaging you, your team, and your business?

And with 28% of workers using AI frequently at work, internal AI slop is becoming a serious problem. I've seen it firsthand. We need a clearer set of norms for how AI should and shouldn't be used in workplace writing.

This manifesto is my attempt to set those norms. First, I outline the ways in which we use AI at work to assist with writing. Then, I'll introduce a new framework for deciding when AI belongs in the process: heart, head, or hands.

Note: This is a living doc. This framework will likely evolve as AI writing changes. Some of today's tells may fade. New ones may appear. We'll see.

How To Decide When To Use AI: The Heart, Head, And Hands Framework

The question to ask is:

Would this person be upset if they found out you used AI to draft this writing?

If the answer is yes!, then it belongs in the heart category. If the answer is a maybe, then it's the head. If the answer is no, it belongs in the hands.

Let's break it down further.

The Heart: You are trying to make an emotional connection with the reader. Treat this as a red light 🚨 for AI.

The Head: The message is partly informational, but trust, judgment, or personal perspective still matter. The other person may care if AI shaped it too much. You may be trying to motivate, build trust, or lead. This is a yellow light ⚠️. Use AI carefully.

The Hands: When the recipient doesn't care if you're using AI. The information is the deliverable, not the relationship. This is operational writing where efficiency matters more than authorship, such as a weekly recap built from spreadsheet data. This is a green light 🟢 for AI.

The heart and hands are fairly straightforward.

Most people intuitively understand the heart category. Few would want AI drafting something like a layoff message. The emotional stakes are too high. At most, AI can offer light feedback that you apply manually.

The head is where things get interesting. There is a lot of "it depends" here. This is where prompting, context building, editing skills, and a solid understanding of AI writing patterns and tells really matter.

My work and recommendations sit in the "head" area. You're using AI somewhere in the process, but you don't want it taking over. Overdo it, and you risk sounding incompetent or triggering a negative reaction in your reader.

Let’s look at examples to make the framework more concrete. The heart and hands are usually easier to spot. The head gets more nuanced, so those examples need a bit more explanation.

Heart (Red): Write it yourself. AI stays out or only gives feedback you apply manually.

1. Announcing layoffs or role eliminations

2. Delivering difficult performance feedback

3. Writing a personal birthday or congratulations message

4. Motivating the team after a rough quarter

5. Welcoming and introducing a new team member

6. Announcing a death or loss on the team

7. Apologizing for a mistake to a client, teammate, or your team

8. Announcing someone's promotion

9. Responding to someone who shared personal news (illness, family situation)

10. Writing a farewell message to someone leaving the company

11. Thanking someone who went above and beyond on something specific

12. Addressing team tension or conflict directly

13. Writing a personal recommendation or reference

14. Delivering bad news to a client about a major failure your team caused

15. A CEO or leader's message about company direction during uncertainty

Head (Yellow): AI can help, but your thinking and voice need to come through. Edit heavily.

1. Sharing a project retrospective with the team - what went well, what didn't, your honest read on why. AI can help you structure it but if your actual observations aren't in there, it's just filler.

2. Introducing yourself to a new client or stakeholder - not a templated email. They're forming an impression of you. AI can help you organize your thoughts but the way you describe your work should sound like you.

3. Responding to a thoughtful question from a teammate - they put thought into asking. A polished AI response feels dismissive even if the content is correct.

4. Writing a follow-up after a networking event or conference - "Great meeting you" emails are a dime a dozen. If yours reads like every other AI follow-up, you've wasted the connection.

5. Pitching an idea to leadership - your conviction and reasoning matter. AI can help you structure the argument but if it rewrites your directness into corporate hedging, you've lost the pitch.

6. Reaching out to someone you haven't talked to in months - the whole point is that you thought of them. If it reads like a template, it defeats the purpose.

7. Giving constructive feedback on someone's deliverable - not a performance review (that's red), but "hey, here's what I think about this draft." Your specific, honest reaction matters.

8. Writing a LinkedIn post about something you learned - your voice and perspective are the product. AI can help you tighten it or create a loose framework but shouldn't write it.

9. Responding to a client asking why you recommend a particular approach - they want your reasoning, not a textbook answer. If it sounds like a Google search result, trust erodes.

10. Sending a proposal or scope of work that isn't templated - especially early in a relationship where they're evaluating you as much as the work.

11. Sharing your perspective on a strategic decision in a Slack thread - people are debating, you're weighing in. If your take sounds like an AI-generated balanced summary, you haven't actually weighed in.

12. Emailing a mentor or someone senior you respect - low frequency, high relationship value. Every word matters more because you don't talk often.

13. A team lead's weekly reflection that goes beyond status updates - when you're not just listing what happened but sharing what you think it means.

Hands (Green): AI can do the heavy lifting. Light editing for accuracy.

1. Weekly or daily status updates in Slack

2. Summarizing meeting notes

3. Documenting a process or SOP

4. Writing step-by-step instructions for a tool or workflow

5. Formatting raw data or analytics into a readable update

6. Creating an agenda for a recurring meeting

7. Writing a bug report or issue ticket

8. Compiling research into a summary

9. Drafting a FAQ document

10. Reformatting messy notes into something structured

11. Creating a project timeline or milestone summary

12. Writing a handoff document when transitioning work

13. Daily standup updates

14. Internal knowledge base articles

15. Templated client updates that go out regularly with new data

The framework above helps you decide when to use AI. The next step is deciding how involved AI should be once it is in the process.

How We Use AI To Assist With Writing

Here are six common AI-assisted writing workflows, ordered from least likely to sound like AI to most likely:

1. You wrote it entirely yourself.

2. You wrote it, AI gave feedback, then you made the edits yourself.

3. You gave AI your ideas, it helped with structure and direction, and you did the writing.

4. You wrote the draft and let AI edit the wording directly.

5. You gave AI your ideas and reasoning, it drafted the piece, and you edited it.

6. An automated workflow pulls in data or research, generates a draft, and you edit the result.

In the examples above, “editing” assumes a relatively light pass.

If keeping your voice matters, use AI for feedback or structure, then make the final wording decisions yourself.

Once AI starts writing for you, you risk falling into its patterns. The output may be polished, clear, and technically “good,” yet still feel too smooth to seem fully yours.

The framework above covers when to use AI. But how you use it matters too. Here are some things I've learned (sometimes the hard way).

What Nobody Tells You About Using AI to Write

Teammates who are skeptical of AI

Spend a little time around pro-AI content online and you quickly see how much resistance and resentment AI can trigger. The reasons vary, and many are personal.

Be kind and patient with your anti-AI teammates. Avoid flooding them with low-quality AI output, and be mindful of how both AI writing and visible productivity gains may land with them emotionally.

AI softening your tone

If you use AI to edit or draft for you, watch for tone softening. Most models default to agreeable, polished language unless you deliberately protect your natural voice.

For example, if you write "This didn't work and here's why..."

With AI it might come out as: "While there were some challenges, there are also opportunities for growth..."

Stick with the original direct and brief tone.

Using AI to inflate weak thinking

AI is great at using a lot of words to say something that has little substance. That is one of the main sources of slop: a lot of words saying very little.

An example of this is using AI to fill out a document when you have little to say. For example, a weak weekly update can suddenly become several polished paragraphs without becoming any more useful.

Another example is an SME filling out a document that the marketing team will use for copy. I've experienced this firsthand. The information mostly comes straight out of the LLM: bland, boring, and basic.

Case studies also suffer here. The best ones include specific details, real tension, and actual lessons learned. If you give it very little solid information, AI inflates it with hollow filler.

Before you hit send...

Ask yourself: would this person care if they knew AI wrote this?

Aim to be the person on the team who 3x’s their output while minimizing the slop.

AI writing etiquette matters because writing is emotional. At work, it does more than transfer info. It shapes how people experience you.

Used well, AI can increase output and creativity while also saving time. Used poorly, it weakens relationships and your impact.

-----

Also, I want to hear from you:

Do you agree with this framework?
Have you developed your own rules for using AI at work?
Do you have any teammates who are using AI poorly, and making things worse?

- Blake 🖋️ 🤖

@blake.stocktonWhich ai writing sign are you suprised to see? 😬 #wikipedia #ai #artificialintelligence #chatgpt #writer #writers #writertok #writersoftiktok

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*There are three more TikTok vids at the end of this post

Wikipedia saw so much AI-generated writing in its articles that editors launched an “AI cleanup” project.

What’s interesting is that the goal of the project is not to ban AI-generated content, but to make sure it meets quality standards. Editors are helping authors revise AI-written text so it fits Wikipedia’s tone and guidelines.

Before we get into the signs of AI writing (what I call “tells”), here are a few things to keep in mind.

A few tips on spotting AI

1. Just because a phrase sounds like AI doesn’t mean AI wrote it. LLMs are trained on human writing, so sometimes people naturally write in ways that resemble AI output.

2. AI detection software is also unreliable. It often flags false positives, which is why human judgment matters more than a score.

Here’s the biggest takeaway from Wikipedia’s new page:

"AI content can have a promotional tone, reading like a tourism website."

I’ve found it to be just as true in B2B writing. AI tends to create meaning, then promote it with intensity. Wikipedia editors explain that this promotional tone often slips through, even with careful prompting.

💡

Tip: Avoid removing every possible tell with prompt instructions. It works better to prompt out only a few things, such as em dashes or parallel negation structures. Too many restrictions make the result stiff or generic, which feels even more artificial.

Let's get into the signs.

1. Inflated symbolism and meaning

AI often exaggerates the importance of a topic by connecting it to broader themes. 

Wikipedia notes that these phrases show up in predictable ways, so editors should consider rewording or removing them.

Phrases to revise or cut:

• is / stand as / serves as a testament

• plays a vital / significant role

• underscores its importance

• continue to captivate

• leaves a lasting impact

• watershed moment

• key turning point

• deeply rooted

• profound heritage

• steadfast dedication

• stands as a

• solidifies

2. Promotional language (around cultures)

AI often writes about cultural heritage with admiration that feels promotional. This tone feels more like travel copy than encyclopedia writing.

Phrases to watch for:

• rich cultural heritage

• rich history

• breathtaking

• must-visit

• must-see

• stunning natural beauty

• enduring / lasting legacy

• rich cultural tapestry

3. Editorializing

AI often adds interpretation or opinion, even when a neutral tone is requested.

Common phrases:

• it's important to note / remember / consider

• it is worth

• no discussion would be complete without

• in this article

4. Overuse of conjunctive phrases (connecting phrases)

Transitions help writing flow, but AI relies too heavily on a small set. Some of these also suggest synthesis or analysis, which does not fit Wikipedia’s neutral tone.

Phrases to watch:

• on the other hand

• moreover

• in addition

• furthermore

• however

• in contrast

5. Negative parallelisms (negation)

Wikipedia highlights a structure AI loves: “It’s not X, it’s Y.” This phrasing creates a contrast that can feel dramatic or persuasive.

Negation is the most prominent AI writing tell. Just a few days ago, I read a prominent newsletter and it was in it five times. Yikes.

A great example from Wikipedia is:

"It's not just about the beat riding under the vocals; it's part of the aggression and atmosphere."

While most people can identify negation in one sentence, it also can be expanded to two sentences, not just one:

"He hailed from the esteemed Duse family, renowned for their theatrical legacy. Eugenio's life, however, took a path that intertwined both personal ambition and familial complexities."

This pattern shows up so often that it’s one of the few worth prompting out. Gen AI will sometimes even use it in back-to-back sentences.

6. Superficial analyses

AI writing tends to comment or analyze information often in relation to its significance, recognition, or impact.

This usually shows up at the end of a sentence with an -ing word, which can feel like empty analysis.

These -ing words often introduce unnecessary opinions. For example:

"Consumers benefit from the flexibility to use their preferred mobile wallet at participating merchants, improving convenience."

Words to watch:

• ensuring...

• highlighting...

• emphasizing...

• reflecting...

7. Vague attribution of opinion

I've personally noticed this issue a lot. Especially if you attached deep research docs to your prompt.

Attributing opinions or claims to a vague authority is a practice called weasel wording. Yes, that's the real term lol.

Words to watch:

• Industry reports

• Observers have cited

• Some critics argue

These phrases suggest authority without clear sources. The writing feels informed, but the support often comes from a single source, or none at all.

8. Excessive use of boldface

AI sometimes uses bold formatting in predictable ways, often to highlight product names or sections. Wikipedia discourages this formatting outside of specific use cases.

In general writing, bold text should feel intentional. Too much bolding creates a pattern that readers associate with AI.

9. Overuse of em dashes

The em dash is a hot topic among writers. Most are angry at Chat for making it seem like every use of the em dash indicates AI writing.

Wikipedia approaches this by saying that AI chatbots use the em dash more frequently than most editors do.

Here is the key phrase, and what you should look out for in your writing:

AI especially uses em dashes in places where human writers are much more likely to use parentheses or commas.

So, if you're using AI to write for you, definitely replace the em dash with parentheses or commas where necessary.

10. Bullet points with bold titles

Bullet points with bolded titles are a common ChatGPT structure, but virtually nonexistent on Wikipedia. The bolded phrase is often just reworded in the sentence that follows, which adds little value.

For example:

• Scalability: The system is designed to scale easily across different use cases.

When this type of bulleting is done consistently, it's an AI tell.

Final thoughts

Wikipedia’s “AI Cleanup” project helps editors recognize and fix AI-generated writing.

It also highlights something important: AI writing tools can help, but the output still needs a human touch.

By learning to spot and revise these tells, we can improve our writing drastically.

More TikTok vids:

@blake.stocktonWikipedia ain’t happy about ai writing signs in their articles #ai #wikipedia #chatgpt #artificialintelligence

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@blake.stocktonPart 3 | Wikipedia’s signs of AI writing: The main takeaway from the examples is the use of superficial phrases with little info depth. Because ai is an okay writer, humans will need to step in to make the writing good, possibly even great. We must include our depth of knowledge and experience. #ai #artificialintelligence #wikipedia #chatgpt #writing #writer #writertok #writersoftiktok #socialmediamarketing #copywriting

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@blake.stocktonPart 4 | Wikipedia’s editors have flagged common AI phrases. The phrases themselves are fine, but overusing them can make writing feel like AI. And remember, Wikipedia aims for a neutral tone. AI often is promotional in tone or tries to add meaning too often or where it’s not needed. #ai #artificialintelligence #wikipedia #writertok #writersontiktok #socialmediamarketing #author #authorsoftiktok #college #collegestudent #studentlife #students #collegetips

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No, that’s not it.

"As businesses navigate the evolving landscape of..."

Still wrong.

"With the rise of AI efficiency..."

Dangit! These intros are the worst.

If you've ever used AI to write, you've certainly encountered its go-to, vague openings that indicate something is changing.

I've read these hundreds of times and still encounter them a few times a week in other people's content. Newer AI users don't recognize it as an obvious AI tell because it was once an okay opening...a few years ago.

Today, it screams AI wrote this. It's always on my edit list.

In this post, I’ll show why AI relies on these openings, what they look like, and how to prompt better intros that sound like you.

Why These Generic Intros Kill Your Content

There are lots of ways AI starts content with a thud, like cheesy metaphors, overly casual questions, and salesy hype.

But this one’s the most common: the vague “something’s changing” setup. I call these vague-change intros.

In the last post we talked about red flag phrases. Well, the "vague-change intros" wear the crown. It's the worst offender. Let's talk about why this opening exists, variations, and how to prompt it out.

Why Gen AI Defaults to These Intros

So why does AI write this type of intro? Two big reasons:

1. It doesn’t know what it’s going to write before it writes it.

Because ChatGPT writes one token at a time, it doesn’t know where the piece is going. So it opens with something safe.

That's one of the huge limitations of gen AI as it writes. Generic openings are a way for AI to “play it safe” when it doesn’t fully understand the direction of the piece. But playing it safe often means writing something that says nothing.

One day, maybe the LLM will be able to think over what it wrote to improve it. Or maybe there will be a truly superior writing software. But, I'm not aware of that, yet.

I want to call out that this process of writing intros last is exactly how I was taught to write. Write intros after you have written the entire piece. Then, you'll have more context for the tone and content.

2. It's trained on boring patterns.

By default ChatGPT likes to play it safe with writing. On the billions of text openings it's trained on, these are the most consistent and safe.

So, you have to prompt to get openings outside of this boring, consistent output.

The more general output is what I call the "Consensus Middle™." This is the zone where most AI-generated writing lives. It’s safe, boring, and forgettable. If you want to stand out, you need to break out of that middle.

Gen AI companies are working on this. Maybe GPT-5 changes the game. But for now, you’re still stuck in the boring middle.

If your examples avoid vague-change language, ChatGPT is less likely to use it. But, it’s not guaranteed.”

What These Openings Look Like

Before we get into the prompting and workflow solutions, let's get surgical on what these vague-change intros look like.

To help you spot them, here’s a list of common vague-change phrases:

1. In today’s competitive business environment…

2. As the digital landscape continues to evolve…

3. With the rise of artificial intelligence…

4. In an increasingly fast-paced world…

5. As organizations adapt to changing demands…

6. With innovation at the forefront of modern business…

7. As companies strive for greater efficiency…

8. In today’s rapidly changing technological environment…

9. As industries undergo digital transformation…

10. With data playing an increasingly vital role…

11. In a world driven by automation and analytics…

12. As the future of work continues to take shape…

For my more formula-minded readers, the formula goes something like this:

As [broad trend] continues to [vague verb], [industry or audience] must [generic goal].

It's a safe formula. Gen AI loves to use it.

I want to quickly call-out that there isn't anything particularly wrong with the formula. But please, please put specifics into the writing. If AI writes it and you like the idea, just make it slightly more specific.

Before and after a vague to specific intro sentences

Before with a vague-change intro:

In today’s fast-paced business landscape, efficiency is more important than ever.

After with a less generic opening:

“Last quarter, 47% of teams missed deadlines due to slow handoffs. Here’s how better systems fix that.”

The first line gestures at a problem. The second one shows it, with real data and a clear point.

Note: I'm assuming you're avoiding the negation format at all costs. It's a given that "it's not x, it's y" shouldn't be in the opening.

Prompting Techniques to Avoid Vague Openings

You can get better intros from AI, you just have to steer it. Here are three ways to prompt around the generic default.

1. Initial prompt nudge

I would recommend a simple prompt nudge from AI. Since intros require a high level of understanding and creativity, you don't want an aggressive prompt that may limit its creativity.

Consider including an initial prompt instruction like:

Avoid vague setups like “In today’s world” or “As businesses strive to” — say something specific, like an example, opinion, observation, or concrete detail.

2. Reference-based prompting

To train ChatGPT on your type of writing, give it a few examples of intros that match your voice, tone, and specificity. You don’t need to explain why the examples are good, just paste them in and say something like:

“Use the attached examples as a reference. Match the tone, structure, and level of specificity.”

This helps the model break out of the “Consensus Middle™” and mimic something more aligned with your actual style. If your references don’t include vague change-setup intros, ChatGPT is much less likely to generate one.

Even 2–3 solid samples can dramatically improve the output.

3. Post draft prompt

My go-to. The intro is so important, I'll spend quite a bit of time trying to get it right.

Once the piece is done, I prompt the AI to revisit the intro now that the full context is clear.

I try to consider the audience and tone. I'll also remind AI of the audience and goal of the content when it's reworking the intro. I may go through 10-20 iterations with direction to get the right info and voice.

Again, I'll say that this process mirrors how a person writes. Write the intro last. Why not treat AI with a similar workflow?

Follow up prompt:

Rewrite the intro now that the piece is complete — open with a real example, stat, or sharp observation that reflects the core message, and cut any generic framing.

Tip: If you start a new chat mid content creation, then you'll want to remind the LLM of who your audience is and purpose of the piece. That will reset its context for the writing.

Final Take

The “In today’s world…” intro is a symptom of vague, predictable writing. AI leans on it when it doesn’t know what else to say.

Don’t let that be your first impression. A strong intro earns attention and builds trust. It tells readers they’re in good hands.

So write the body first. Then write the intro with purpose. Give it your voice. Make it count.

Red flag phrases are high-probability outputs that sound like something you’ve read a thousand times. Because you probably have.

Think of lines like:

"In today's world..."
"Optimize your work flow..."
"Let's dive deep into..."

They’re not technically wrong. But when they pile up, your content starts to sound like it was written by a machine. And if you stack two or three in a single paragraph? You’ve got a red flag.

These phrases drain personality from your writing. They make you sound like everyone else, even when you’ve got something valuable to say.

This post breaks down why AI leans on these phrases, how to prompt around them, and which ones to look out for.

Why do we get repetitive, common phrases from AI?

LLMs choose to output what is statistically safe. Not what is stylistically fresh.

Let me explain.

We have to remember that LLMs are prediction tools. Based on its training material, it chooses language that is statistically safe and likely to satisfy the user's prompt request.

That's where we get the "consensus middle" with platforms like ChatGPT, where outputs tend to sound the same.

Additionally, when you start to provide context in your prompt, like the audience and writing voice, ChatGPT further narrows the words and phrases of your output.

If you read blogs or posts in your niche, you’ve probably seen the same phrases pop up again and again. Especially if AI was involved.

A Simple Framework to Handle Overused Phrases

Rather than outright banning phrases from the start, use this prompt flow to nudge, identify, and rework AI-written content with a lighter touch.

Step 1: Nudge the AI Early

Start by gently discouraging overused phrasing before the draft is written. Add these instructions to your prompt. If you’re using a more structured prompt, include this in something like the “context package” or “directions” section.

Prompt:

Avoid overused phrases like “in today’s world,” “game-changer,” “let’s face it,” or “the bottom line.” Use specific, natural language and varied sentence rhythm. Make it sound more like a human than a content formula.

Optional addition:

You can use common structures, but don’t lean on clichés. Surprise me with at least one turn of phrase or transition.

Step 2: Identify AI-ish Phrases

After generating a draft, prompt AI to spot weak or generic language. This will help you identify any phrases you may have missed. If you're new to the industry, you'll want to identify these.

Prompt:

Highlight any phrases that sound formulaic, overused, or like AI-generated filler. Focus on intros, transitions, and overly polished marketing language.

Step 3: Rework Problem Areas With Better Prompts

Now that the issues are visible, experiment with these prompts to rework the content based on tone and context.

General Cleanup

Rewrite this using clearer, more specific phrasing. Avoid buzzwords like “unlock,” “real value,” and “future-proof.” Keep the tone direct and grounded.

*You can replace the buzzwords that are specific to your industry.

Make It Sound Human

Make this sound like something a real person would write. Avoid startup clichés, empty transitions, and overly neat summaries. Let the rhythm feel a little more conversational.

*Replace "startup" with your niche/industry.

Add More Personality

Rewrite this in a confident, human voice. Be specific. Be interesting. Cut phrases that feel generic, like “the bottom line” or “let’s dive in.” Use natural transitions and avoid trying to sound “smart.”

Open Stronger

Rewrite the intro without using phrases like “In today’s fast-paced world…” or “Let’s face it.” Start with a clear problem, real moment, or sharp observation instead.

Reworking the opening sentence or paragraph is likely something you'll need to do for every writing. ChatGPT doesn't know what it's going to write. It learns as it writes. So, after it writes the content, ask it to reconsider its content and rework the introduction.

Common AI Phrases I've Come Across

Alright, so here's a comprehensive list of repetitive phrases I've come across. Most of these phrases are fine to use, (aside from a couple such as "in today's fast-paced..." and its variations), remember to consider how these flow within your content.

Templated Intros & Transitions

These phrases feel like they were pulled straight from a content formula. They signal structure, but lack originality.

• “In today’s fast-paced [world/landscape/business environment]…”

• “In a world where…”

• “As the [industry/landscape] continues to evolve…”

• “Now more than ever…”

• “Let’s dive in.”

• “Let’s break it down.”

• “Here’s the thing…”

• “Here’s the uncomfortable truth:”

• “The goal?”

• “The result?”

• Intro formula: “As [trend/movement], [topic] is becoming [value statement] in [context]”

Business Hype / Sales-y Language

These phrases try to sound impressive or persuasive but feel empty or inflated, especially when overused.

• “Forward-thinking companies…”

• “This shift in how forward-thinking companies…”

• “Revolutionizing the way…”

• “A game-changer for [industry/role]”

• “That’s where [product/solution] comes in.”

• “Unlock the power of…”

• “Supercharge your [workflow/team/performance]”

• “Transforming [X] into [Y]”

• “Simply cannot [match/achieve/replicate]”

• “Modern” / “Today’s” [professionals/businesses/solutions]

• “Future-proof your…”

• “Stay ahead of the curve.”

• “Strategic advantage” / “Strategic benefit”

• “The bottom line…”

• “Thrilled to announce…”

• “Imagine a world where…”

Faux-Conversation

These try to mimic a casual, human tone but often feel forced or unnatural.

• “Let’s face it…”

• “But let’s get real…”

• “What does this mean for you?”

• “Not all [X] are created equal.”

• “It’s no secret that…”

• “Whether you’re a [persona A] or a [persona B]…”

• “I hope this message finds you well”

• “Unpack that”

• “The good news?”

• “The real unlock”

• “Real problems” / “Real value” (when nothing is really “real”)

Repetitive Structures

These aren’t phrases themselves, but templates AI tends to overuse to sound neat or structured.

• “[Problem]? Meet [solution].”

• “It’s time to…”

• “Why it matters…”

• “Here’s what you need to know…”

“Do X, so you can Y.” Example:

“Automate your processes, so you can focus on what matters.”

“X is more than just Y. It’s Z.” Example:

“It’s more than just automation. It’s a complete transformation.”

Wrap-up

Red flag phrases aren’t always bad. But they’re rarely great. When too many stack up, they flatten your tone and blur your voice. The fix isn’t complicated. Just notice them, prompt around them, and rework where needed. It’s one of the simplest ways to make AI-assisted writing feel more like your writing.

I know that can be confusing. The idea of context is especially true when it comes to word choice.

I don’t like when people make strict lists that ban AI from using certain words.

Why shouldn’t AI use a word if it’s accurate and helpful to the reader?

But ChatGPT can over use words, especially when it's for specific industries or explanations.

Think "unlock" for anything AI.

Or "empower" when talking about users or teams.

Or "robust" when talking about features.

That’s why we’ll call them red flag words. Just because you see one doesn’t mean AI wrote it. But it’s a red flag. Too many red flags, and it’s a red alert.

In this nuanced post, I’ll cover:

• Why common AI words are a problem

• What to do about them

• 3 prompts to help you manage them

Why Common AI Words Are a Problem

The words you use cause an emotion in your reader. The main problem I have with common AI words is they can trigger a range of negative emotions in your reader.

Here are some examples of common AI word choice that may trigger negative feelings:

1. They’re industry clichés.

These words are so overused in certain fields that they lose meaning and make your content blend in.

2. They’re vague and unspecific.

Words like “transformative” or “innovative” sound impressive but rarely point to anything concrete.

3. They show up across brands and voices.

The same language can appear everywhere, making it hard to tell one company or writer from another.

4. They create a false sense of clarity.

The writing looks polished, but the words don’t actually say much once you look closer.

What to Do About Common AI Words

In what I call the consensus middle, the LLMs chooses words that are typical.

Imagine a distribution curve filled with all the possible words AI could use to describe a topic.

The center of that curve is where the most common, “safe” words live — the ones the AI is most likely to choose.

For example, if you’re writing about change, ChatGPT will probably use transform or shift. These words are high-probability choices for describing change.

It's unlikely to output words like "upended, restructured, or morphed."

Additionally, LLMs prioritize concise language. So it's also unlikey to use a longer phrase to explain change, such as "give way to."

The Mindset

The problem with telling ChatGPT “don’t do this”

What I’ve found is that asking ChatGPT not to do something often limits its creativity. And I don’t like that. I want the words and sentences to be crafted with full creativity, then I can edit them as needed.

So as a general rule, I focus less on telling AI what not to do and more on nudging it toward what I want.

3 Prompts to Navigate Common AI Words

1. Identify what ChatGPT defaults to

This is part of what I call meta-prompting, where you ask ChatGPT to analyze itself.

Try:

What words or phrases does ChatGPT commonly use when writing about [TOPIC or INDUSTRY]?

Please include overused verbs, transitions, buzzwords, and clichés. Separate the list by type if possible.

Or:

List 20 words or phrases ChatGPT is most likely to use when writing about [TOPIC] in a professional tone. Group them by function such as verbs, adjectives, transitions, and value language.

2. Guide ChatGPT with a soft constraint

Once you’ve identified the common words, add them into your prompt like this:

Be mindful of common industry buzzwords like [LIST], and try to vary the language so they aren’t overused.

This gives direction without being too restrictive.

3. Block specific words if needed

If the soft prompt doesn’t work (and AI insists on using them — some AI habits are hard to break), you can be more specific:

Avoid these words: [LIST]. Use specific, concrete language and vary sentence structure to sound more human.

What Happens When You Ask ChatGPT for Its 2nd or 3rd Word Choice

Now, I’ve seen multiple people recommend prompts that ask ChatGPT to use words it’s less likely to think of.

The issue with this (and I’ve tried it myself) is that the sentences start to sound like you’re visiting crazy town.

It’s okay to use a unique word like morph to describe change. But when ChatGPT starts stacking unusual word choices back to back, the result is awkward, clunky, or even laughable.

Here’s what I mean:

Default (consensus middle):

The company transformed its operations to stay competitive in a changing market.

2nd or 3rd choice prompt (what people recommend):

The company morphed its internal systems to remain nimble in an evolving landscape.

That second sentence sounds like it’s trying way too hard. It’s technically “different,” but it doesn’t feel natural or helpful to the reader.

If you start writing too much of the second or third-choice word set, you’ve hopped on a quick train ride to crazy town.

List of Common AI Words

Here’s my personal list. If I see too many of these in a sentence or paragraph, it’s usually a giveaway that AI wrote it.

Innovation / Change / Strategy

• Unlock

• Transform / Transforming

• Revolutionize

• Future-proof

• Game-changer

• Strategic

• Shift / Shifts

• Modern

• Today’s

• Real (as in “real benefits” or “real change”)

• Supercharge

• Harness

• Leverage

• Optimize

• Streamline

• Fundamental / Fundamentally

• Unleash

• Enhanced

• Unprecedented

• Seamless

• Powerful

• Intuitive

• Comprehensive

• Tailored

• Scalable

• Agile

• Dynamic

• Streamlined

• Cutting-edge

• Best-in-class

• Next-generation

Business / Value Framing

• Enable

• Crucial

• Essential

• Key

• Robust

• Distinct

• Important

• Strategic (again)

• Elevated / Elevate

• Aligned / Align

• Proactive

• Explicitly

• Nuanced

• Innovative

• Intersection

Conversational or Transitional Cues

• Moreover

• The goal?

• The result?

• The bottom line

• Fundamentally

• Thrilled

• Delve

• Foster

• Emphasize

Descriptive Overuse or Misuse

• Staff (used repeatedly instead of “employees”)

• Real (again: “real-world,” “real problems”)

• Today’s / Modern

• Strategic (repeated here for how broadly it’s applied)

Words That Signal Hype or Persuasion

• Unlock

• Crucial

• Thrilled

• Transform

• Revolutionize

• Supercharge

• Game-changer

• Future-proof

• Unprecedented

• Real

• Cutting-edge

• Best-in-class

• Powerful

• Next-generation

Wrap-Up

Common words aren’t automatically bad. But if your writing leans heavily on them, it starts to feel empty, generic, and machine-written. Awareness is the first step. Then comes control.

Colons are great. I love colons. Even the one in your gut serves a beneficial purpose. But when ChatGPT overuses those two dots in your writing, it starts to feel…robotic.

Because generative AI aims for efficiency, you’ll often see a lot of colons in its output. They do a few things AI really likes:

• More concise. Colons let AI be brief and direct.

• Easy cohesion. They connect ideas without needing transitions.

• Definitions. AI loves to explain. You'll often see a sentence like:
  Here are the benefits: cost savings, flexibility, and speed.

• Instructional tone. AI slips into teacher mode fast. But if your tone is meant to be human or persuasive, the colons can pile up.

• Low risk. A colon is safer than crafting a nuanced sentence. For example:
  The solution is simple: automation.

The Problem with Colons

Again, the issue isn’t that ChatGPT uses colons. It’s that it can use a lot — and I mean a lot — of colons.

Where colon overload shows up

1. In multiple paragraphs in a row.
You’ll sometimes see three paragraphs in a row with colons. AI loves consistency. But readers notice when every thought follows the same structure. It feels flat and repetitive.

2. In a paragraph and the bullet list lead-in.
Example:

Your content needs to do more than just inform: it should engage, persuade, and guide action. That kind of impact doesn’t happen by accident. The best content teams focus on three key areas:
- Strategy
- Voice
- Distribution

Personal opinion, this is just bad taste. If I see this structure — colon mid-paragraph, then another colon before bullets — I’m raising the red flag for AI-generated writing. 

💡

Best practice: keep the colon in the bullet list lead-in or the paragraph, not both.

3. Alongside other AI writing tells.
Here's a sentence that screams “I was written by ChatGPT”:

Here’s what you need to know: AI isn’t just a trend—it’s a competitive advantage.

It’s got all the signs:
– A colon
– A negation structure (it's not X, it's Y)
– An em dash

4. In every header.
Ask ChatGPT for blog headers, and you’ll get something like this:

– Why AI Writing Still Feels Robotic: The Real Culprit Behind the Tone
– Fixing Your Content Flow: A Practical Guide to Better Transitions
– The Power of Prompting: How to Get More Human Output from AI

Every header has a colon. It’s not wrong, but it’s predictable. And that predictability is a giveaway.

The Solution

Should you prompt ChatGPT to "use fewer colons"? Honestly, I don’t recommend it.

Prompts that tightly control style often limit the creativity of the output. I’d rather get the most compelling content first. And then clean it up.

What I do instead:

• While editing in ChatGPT or Claude, I highlight a section and ask the AI to rework it without the colon.

• If I see colons in three paragraphs in a row, I revise at least one.

• If a colon shows up right before a bullet list, and another one just showed up in the same section, I remove one.

• If every header has a colon, I’ll ask for alternative headings, just without colons.

However, if you do find that AI is including too many colons in your drafts, here is a prompt before you generate:

As you write, aim for natural transitions instead of relying on colons. Vary sentence structure to avoid colon overuse, especially in headers and list lead-ins.

And here is a prompt for your rework phase:

Review this for overuse of colons. If there are multiple colons in a row, or one in a paragraph followed by another before a bullet list, rework the structure to reduce repetition and improve flow.

Wrap-up

I’m not anti-colon. But I am anti-pattern.

Colons are useful. Just don’t let them become your writing’s default move. If it starts to feel repetitive or robotic, it probably is.

Catch it. Cut it. Or ask ChatGPT to rework it for you.

You may have run into sentence stacking.

This happens when writing sounds robotic. Every sentence stands on its own. There's no rhythm, no buildup. Just one flat fact after another.

An example of sentence stacking to explain improv:

Improv is a type of unscripted theater. It requires performers to make up scenes in the moment. Many exercises focus on listening and reacting. One popular rule is to say “yes, and” to keep scenes going. Improv helps build confidence and creativity. It’s often used in team-building workshops and acting classes. There are short-form games and long-form formats.

Here's how you might rework the paragraph:

Improv is a form of unscripted theater where performers build scenes in real time. It sharpens listening, encourages quick thinking, and builds the kind of trust that teams can feel on and off stage.

You see how the paragraph goes from information on information without any framing to something more cohesive? If your paragraph feels like a list without bullets, it probably is sentence stacking.

Often, you'll feel sentence stacking before you realize it. The writing will feel robotic. Boring.

Your Sentence Stacking Checklist

Let's make sure you're a pro in identifying sentence stacking. You'll know the writing is sentence stacking and you'll need to rework it when it has:

• Every sentence stands alone. Nothing builds on what came before it.

• The rhythm is repetitive. Most sentences follow the same short, factual structure.

• There are no bridges. Transitions like “so,” “however,” or “as a result” are missing.

• No voice or perspective. There’s no point of view, story, or emotional texture.

• It reads like a list — just without bullets.

The #1 Problem With Sentence Stacking

There are several problems with sentence stacking, but here's my main one: It leads to cognitive overload in the reader.

When information is presented that dry and robotic, it's difficult to process. It's like a list of facts with little connection.

Your brain doesn't like it. You check out. You're moving on to better more interesting topics like what's for lunch.

Even worse once cognitive overload sets in, it creates negative feelings in the reader: Frustration and overwhelm.

And those are the last emotions you want your readers to feel.

Great content writing flows easily in the readers' mind. There are no hang ups or stumbles. It just flows. Almost hypnotic.

That's a goal of writing, including B2B writing. Content that feels good to read.

So let's consider a couple AI triggers that lead to sentence stacking...

1. Sentence Stacking Trigger: RAG

One common trigger for sentence stacking is RAG, or retrieval-augmented generation. For RAG, you feed the AI a document to help it generate content.

For example, you might upload a long voice note full of ideas, a transcript from an SME interview, or a detailed product fact sheet.

After the upload, you prompt the AI to create a piece of content based on the document uploaded.

The problem with that prompt is the model doesn’t know what matters most. So it tries to include everything in the document. The result is information-dense writing with no flow, no pacing, and no clarity.

It gets worse if you add a word count to the prompt. The model now tries to compress all that input into a small space. That leads to short, disconnected sentences. It sounds robotic, and the ideas feel stacked instead of connected.

2. Sentence Stacking Trigger: Bullets Rework

Another common trigger? Asking AI to rewrite bullets as a paragraph.

If you don’t tell it how you want the ideas to flow, it just turns each bullet into a short sentence with no transitions, pacing, or hierarchy.

A better prompt would be: “Turn these bullets into a cohesive paragraph with voice and varied rhythm. Use transitions to guide the reader.”

3. Sentence Stacking Trigger: Prompt Constraints

The third scenario you may see sentence stacking is from your own prompting.

Here's an example prompt that could trigger sentence stacking:

You’re an expert with 20 years of experience. Write a short, actionable summary of how improv helps with communication in under 120 words.

Why this prompt could trigger sentence stacking:

1. "Expert with 20 years of experience" defaults to an overly formal, neutral, and authoritative tone

2. "Make it actionable" risks an instruction style writing with dry how-to steps. The writing may front load with clarity, rather than rhythm.

3. "Keep it under 200 words" compresses ideas into short, discrete sentences to meet the word count.

How to Fix Sentence Stacking

Sentence stacking can happen for different reasons, but your writing goal is always the same: connect ideas in a way that feels natural and keeps the reader engaged.

Prompt Fix #1: Prioritize Before You Generate

Prompt:

From this source, choose 3 to 5 topics that would matter most to [target audience]. Go deeper on those, and don’t try to cover everything.

Why it works:
It prevents the model from trying to say too much at once. When you give it permission to focus, the writing becomes more meaningful and less robotic.

Prompt Fix #2: Ask for Flow, Not Just Clarity

Prompt:

Make sure the writing uses natural transitions and reads smoothly. Avoid stacking short, factual sentences. Connect ideas in a way that guides the reader through the topic.

Why it works:
Most prompts focus on what to say. This one also tells the AI how to say it, which improves pacing and structure.

Edit Fixes (Don’t Skip These)

Even with a good prompt, you might still get some stacked output. Here's how to improve it during editing:

Edit Fix #1: Combine ideas

Short, factual sentences often belong together.

Improv builds confidence. It helps people think on their feet.

Becomes:

Improv builds confidence by helping people think on their feet in unpredictable situations.

Edit Fix #2: Add transitions

Use phrases like “For example,” “That’s why,” or “As a result” to connect ideas and guide the reader.

Edit Fix #3: Vary sentence rhythm

Mix short and long sentences to keep things moving.

Edit Fix #4: Add voice or perspective

Include a point of view, real-world reference, or casual aside.

The Sentence Stacking Fix Formula

As a short hand, here's the main takeaways to avoid sentence stacking:

Prompt for flow → Prioritize → Use transitions → Vary rhythm → Add voice

Additionally, read your paragraph out loud. If it sounds like a list or feels like it’s missing rhythm, it probably needs a rewrite.

Wrap Up

As someone with a teaching background, I love when writing flows naturally and smoothly. When I use transitions and connect ideas well, it feels good to read. And that's what we're aiming for as AI-assisted writers — writing that feels effortless to the reader.

One of the most beloved writing techniques of AI is negation. This is when AI writes something like:

"It's not just X, it's Y."

"We're not just building a product, we're creating an experience."

Or throw in an em dash:

"Credit card fraud isn't just evolving—it's accelerating!"

More technically, this writing structure is called a "contrastive structure" (per Claude) or a "contrastive reframe" (per ChatGPT).

Whatever you want to call it, the negation structure is everywhere. I feel like I'm in my own little Black Mirror episode.

Just this morning, I check LinkedIn. It was in the third post, discovered in less than 30 seconds:

"The teams who will thrive now aren’t just using AI for speed. They’re combining it with judgment, experience and original thinking."

(Aside: The combination of the negation and the triplet series is very AI.)

Let's check X. On the fourth post I saw negation in a very cool looking visual:

Then, back on LinkedIn, I discovered an article on a personal blog. The blog is about how companies should be more lenient on AI-detected writing. The idea is that good writing is good writing. And I agree. All is well so far.

However, when I got to the takeaways section at the end of the blog...negation everywhere. Three instances in 250 words:

AI doesn’t eliminate labor; it redistributes it. 

This isn't a retreat from technology; it's an evolution enabled by it.

And a little longer:

Not performative updates — but real transparency that restores confidence in both the process and the person.

(Aside: The last one gets bonus points for using the em dash with negation.)

You may be thinking, Blake, you're way too into this negation thing. And I agree, I'm ahead of the curve and more concerned because I'm studying it. I'm looking for it. Like seeing more red Ferraris on the road after I bought a red Ferrari. (I didn't buy a Ferrari.)

However, here's the problem. I think in 6-12 months years people will be more sensitive to negation because like I just showed you, it's everywhere.

Once you read the contrastive structure hundreds of times in blogs, ebooks, social posts, emails, newsletters, ads, you start to get a negative reaction. Like actual negative emotions to the writing technique: frustration, annoyance, and a bit of rage. I know I do.

Now, I'm not saying you should never use negation. What I am saying is that it needs to be done tastefully. Before we talk solutions, let's talk about why AI is using this structure.

There are many reasons, but I want to highlight a couple.

I asked Claude why it uses negation in its writing. It gave several reasons, but the summary was telling: Negation adds depth to statements, making content sound more sophisticated.

And that, friends, is one of the larger issues with AI writing. It tries to sound sophisticated without being sophisticated. Tuck that thought away for future blogs and videos.

Specifically for B2B writing, negation sentences (per Claude):

• Elevates the product from a simple utility to a strategic solution

• Shifts focus from features to benefits or outcomes

I agree. These are two strategies to make a product or idea appear more sophisticated. But using negation via AI doesn't lead to quality writing.

So how do we as content professionals solve for negation structure?

I have a few solutions for you.

First, we need to prompt out negation in the original prompt. Here are a few prompt examples:

"Avoid any sentence structures that set up and then negate or expand beyond expectations (like 'X isn't just about Y' or 'X is more than just Y' or 'X goes beyond Y')"

Or you can give AI slightly more direction in the output. You can use a prompt like:

"Avoid any sentence structures that set up and then negate or expand beyond expectations (like 'X isn't just about Y' or 'X is more than just Y'). Instead, use direct, affirmative statements. Feel free to be creative with your sentence structures and expression styles."

I, personally, like shorter prompts that enable more creativity in the LLM so the output isn't too narrow. I like how the second prompt calls out being creative. However, depending on your other prompts, the output may be too varied. Try both prompts to see which output you like better.

Second, add negation to your Content Review Checklist.

I'll often search for "just" in the content to see if any obvious negation snuck in. I'll also ask AI to check the content for instances of negation.

Again, I want to reiterate that negation can be in content. But, it needs to be tasteful.

Third, if you want to include negation in your writing, I'd recommend a couple strategies. Instead of making generic statements, make it more specific. The writing will be more lengthy, but detailed.

For example, instead of:

"AI doesn’t eliminate labor; it redistributes it."

Try something more specific:

"AI won't eliminate your B2B writing job. It will become your research assistant, handle your first drafts, optimize your content for SEO, and free up your time to focus on the strategic messaging and emotional appeals that convert enterprise prospects into loyal clients."

Additionally, you could make the negation unique by including personal experiences. For me, this is the preferred writing strat. Speaks to the author's direct experience.

Alright, I think that's enough, for now, on negation in AI writing.

So remember: We're not trying to remove all negation from writing, we're simply elevating writing to a level that would make Hemingway rise from the grave and slow clap.

About a year ago, I built a GPT to help professionals tell better stories. At the time, it was just okay—maybe a 3/10. The user experience wasn’t great, and the output felt mediocre at best.

Recently, I decided to go back and rework it, and now? I’d say it’s a solid 5 or 6/10. Still room for improvement, but it’s definitely better.

Along the way, I noticed a few surprising things about how it improved. I’ll share those, and at the end, I’ll talk about what it would take to push it to an 8 or 9/10.

Three Things That Surprised Me

1. Customized Examples (Without Being Told)

One of the biggest surprises was how the GPT started offering custom examples based on just a little input. I didn’t even explicitly program it to do this in the instructions or knowledge file—it just started behaving like a real coach. That was a huge leap from the earlier version.

2. Positive Reinforcement

I had asked the GPT to summarize the user’s input before giving feedback, but the way it did it felt natural—like a coach encouraging you. It didn’t just parrot back what was said; it framed it in a way that reinforced the storytelling process.

3. Recognizing Unanswered Questions

Another big improvement? It noticed when I skipped a question. For example, it asked, What was the end result of your efforts? and How did this experience change you personally?

I answered the first part but skipped the second, and instead of just repeating the question, it reworded it in a way that nudged me to answer: On a personal level, how did this change you?

The old version wouldn’t have done that.

How to Get from 6/10 to 9/10

Right now, I think this GPT has reached its limit. Adding more tweaks wouldn’t improve it much. Instead, the next step is to turn this into a structured workflow—a prompt chain.

A prompt chain is when you interact with an AI multiple times in a structured way to refine the output. This would mimic how a real storytelling coach works:

1. Start with the basics – Get the bones of the story down.

2. Refine key elements – Address common storytelling struggles.

3. Add depth – Use AI to help with things like:

   • Inner transformation – Many people struggle to highlight personal growth in their stories.

   • Sensory details – People often tell instead of showing. AI can nudge them to add richer descriptions.

This kind of multi-step process would take the GPT from decent to actually helpful.

If you’re looking for a storytelling AI, I’ll drop the link to the GPT in the comments. Give it a try and let me know what you think!

What's the best strategy to convince them they should buy?

List product features? No.

State factual numbers? No.

Show fancy charts? No.

Share a story? Yes.

Why is a story the superior way to persuade and sell?

A well-told story subtly imparts lessons, a method called 'teaching without telling,' which is subtle yet powerful.

What does 'teaching without telling' mean?

It means illustrating a product's benefits through a narrative, rather than asserting it directly.

Sharing a story demonstrates how someone discovered these benefits. The listener gets to relive the experience to better understand the product.

The listener learns through osmosis.

What a strange word, osmosis. What does it mean?

Osmosis, in this context, refers to the gradual and often subconscious absorption of ideas.

Imagine osmosis as a gate that controls the flow of a crowd; the wider it opens, the more people can pass through, similar to how effective storytelling allows more persuasive information to reach the audience."

Storytelling opens the gate wide, allowing a rush of persuasive information to pass through more effectively than any other communication method.

In addition to the osmosis factor, another factor that makes storytelling a powerful way to persuade is its sticky factor.

Stories stick, they're memorable.

CONFLICT

Now, let's discuss how conflict makes stories stick.

Stories stick in our minds primarily because they involve conflict, which evokes emotion. When there's conflict, there's emotion.

Think of conflict as anything keeping someone from achieving something. Conflict can be physical, like a storm, or abstract, such as fear.

The larger the conflict, the more emotionally invested we are in the story.

For example, we care more about someone striving to sustain their business during an economic depression than during a slow sales day.

The conflict is larger.

Another way to increase emotion is to raise the stakes, which are what will happen if the person doesn't overcome the conflict?

Consider the stakes involved in missing a deadline: a $50 fine is trivial, but a $500,000 fine could bankrupt a business and ruin a family? Now those are some large stakes.

A Simple Story Structure

How do you craft a simple story with conflict? Four elements:

• A character (aka person)

• A goal (aka desire)

• A barrier (aka conflict)

• A solution

Said in a sentence: A character has a goal and a barrier is keeping them achieving that goal until they find a solution.

The story is how you craft these four elements.

For example:

A CTO (character) is trying to implement a new CRM software (desire), but the software available is too expensive for their needs (barrier) so they made an in-house software (solution).

A different barrier and solution:

A CTO (character) is trying to implement a new CRM software (goal), but the employees are struggling to learn the software (barrier) so the CRM software company creates new tutorials (solution).

When crafting a story to tell, start with these basics.

Another way to think about a story is like a journey. The CTO is going from point A (no CRM software) to point B (employees happily using the CRM software). What barriers and solutions happen from point A to point B?

A simple story has one barrier. A more complicated story has multiple barriers. More barrier often means a more interesting story.

When you watch a movie, there is a ton of conflict. Typically, every scene will have a conflict between two people. One person desires one thing, while the other desires another.

For example, one character wants to go off and fight, and the other character wants them to stay home. They argue about it, and someone wins the argument.

Make The Character Relatable

So we have conflict, ideally, decently big conflict with stakes people care about. What else do we need to increase emotion and make the story memorable?

We need to make the audience care about the character.

How do we do that?

By making the character relatable. Here's how to do that. When setting up the story, talk about how the character:

• Deserves sympathy

• Is in jeopardy

• Is likable

• Is funny

• Is powerful

Let's break these down real quick because they are slightly confusing.

"Deserves sympathy" means someone is facing a circumstance due to no fault of their own, such as a rival stealing a product idea. We naturally root for someone dealing with undeserved misfortune. It naturally creates emotion.

"Is in jeopardy" means the person is facing danger, such as potentially losing a business location due to a steep rent increase. The danger doesn't have to be physical, it could be emotional or psychological. When in danger, the audience naturally worries for the character.

"Is likable" means the character engages in behaviors that are naturally admired, such as kindness, loyalty, or bravery. An easy way to be likable is to show consideration for others, such as mentoring new employees and showing genuine care for employee growth.

"Is funny" means humorous and could be a sharp wit, such as someone who lightens the mood on serious topics. Someone who can ease tension while also being respectful of others.

"Is powerful" means someone who is good at their job. We like those who are good at what they do. For example, a manager with creative strategic thinking who can turn around struggling marketing campaigns.

Now, typically, the more of these relatable factors you can use when setting up a story, the more we will relate and connect with the character.

Setting up these characteristics is not easy to do. Typically, in a good movie, they will use at least three of these to introduce a character.

Many poorly rated movies don't do this for the main character, also called the protagonist. Because the protagonist isn't relatable, we end up not caring for the protagonist, we don't root for them to overcome the conflict.

Next time you watch a movie try to identify these characteristics, typically within the first ten minutes of the movie.

💡

Often, people try to make a story about a company or a team. I don't recommend that. We need to hear about a person or people on that team. Yes, Hollywood tells a story about a team trying to do a heist. However, we know about the struggles and desires of the people on that heist team. A story about a company or team is going to be much less memorable, and therefore less effective than a story about a person.

The Inner Journey

To make a story more persuasive, the last aspect of storytelling to consider is the "inner" or "emotional journey" that occurs in the character.

I find the inner journey the most difficult part of a story to craft. But when done well, the inner journey is what will make the story memorable for years.

Yes, literally years.

The "outer" journey is what we've mostly been discussing. The outer journey is the visual goal the character would like to achieve, which could be to make more sales, complete a project, or secure a crucial business investment.

Typically you can see the outer goal either in person or on paper.

The "inner journey," in contrast, is the character's emotional and psychological growth throughout the story.

Often in a movie, the main character lacks a quality and the conflict in the story helps them develop that inner quality.

For example, if the outer journey is securing a business investment, the inner journey is learning to trust your instinct, something you've struggled with your entire life. Another example is overcoming a deep-seated fear of failure that they've lived with.

Discussing this inner transformation is what creates a deep and long-lasting connection with the audience.

The audience feels sympathy for you and is rooting for you.

Now, there's an art to the inner journey. Maybe you shouldn't share all the details of why you have a deep-seated fear of failure. Particularly in a business context. However, some type of inner journey should be shared.

An example of a softer inner journey may be to become a successful business owner to provide for your family and sick mother.

My only tip for crafting the inner journey is that you have to know emotions well. If you struggle to identify emotions, particularly within yourself, it's going to be difficult to craft the inner journey.

When crafting the inner journey, literally look at an emotions wheel and use emotions from it:

The Finale

Now that you have the basic elements of a story, are you ready to start crafting?

Well, maybe not yet. Crafting stories is really difficult, it's often something we don't do in our day-to-day. We consume a lot of stories in movies and shows and the news. But we don't craft like we used to before the influx of entertainment at our fingertips.

At first, writing and telling a story may feel awkward. It's a new way to communicate information. Especially, if you include emotion and aren't used to communicating in that way. It definitely wasn't and still is a struggle for me.

However, I hope you get to experience the feeling I have when telling a story. Telling a story grabs attention and keeps it like no other communication method. It connects with people like you never have before.

And when you get the storytelling elements in place, it feels natural, like it's something you should have been doing all along.

Keep learning story, it's worth the investment.

Use this list to inspire your topic when creating a business story:

1. The Origin Story

• Beginning: Start with the origins of the company or product, highlighting the initial problem or need.

• Middle: Describe the journey of finding a solution, including the challenges faced and the breakthrough moments.

• End: Conclude with the success of the current state, showing how the company or product has evolved and its impact on the market.

Purpose: This story type helps to build brand identity and credibility by showcasing the company’s roots and evolution.

2. The Customer Success Story

• Beginning: Introduce the customer, their background, and the specific problem or challenge they faced.

• Middle: Explain how they encountered the product or service and the process of implementing it.

• End: Highlight the results and benefits experienced by the customer, backed by data or testimonials.

Purpose: Customer success stories are powerful for illustrating the value and effectiveness of a product or service, making them essential for sales and marketing efforts.

3. The Vision Story

• Beginning: Present the current state of the industry or market, including the main challenges or opportunities.

• Middle: Share the vision for the future, detailing how it addresses these challenges or capitalizes on opportunities.

• End: Describe the path to achieving this vision, including the role of the company/product and the call to action for the audience.

Purpose: Vision stories inspire and motivate the audience by painting a picture of a better future and showing how the company’s offerings are essential in realizing it.

4. The Challenge Story

• Beginning: Set the scene by describing a significant challenge or obstacle faced by the company or industry.

• Middle: Narrate the steps taken to address the challenge, including any setbacks and how they were overcome.

• End: Conclude with the outcome, focusing on the lessons learned and the strengths demonstrated.

Purpose: Challenge stories demonstrate resilience, innovation, and the ability to overcome adversity, which can build trust and admiration among the audience.

5. The Transformation Story

• Beginning: Outline the ‘before’ state, including the initial situation and its limitations or problems.

• Middle: Describe the journey of change, including key actions taken, milestones reached, and challenges faced.

• End: Showcase the ‘after’ state, emphasizing the positive changes and improvements made.

Purpose: Transformation stories highlight the journey of significant change and improvement, demonstrating the company’s capacity for growth and development.

6. The Innovation Story

• Beginning: Start with the status quo or a common issue in the industry that needs addressing.

• Middle: Describe the innovative approach or technology developed to tackle this issue, including the creative process and any initial skepticism or obstacles.

• End: Highlight the success and impact of the innovation, with examples of how it’s changing the industry or market.

Purpose: Innovation stories demonstrate the company's ability to think outside the box and lead change, showcasing its value as a forward-thinking entity.

7. The Partnership Story

• Beginning: Introduce the partners, their backgrounds, and the reason for their collaboration.

• Middle: Detail the process of working together, including the synergies created, challenges faced, and how they were resolved.

• End: Show the outcomes and benefits of the partnership, for both the partners and their customers or clients.

Purpose: Partnership stories illustrate the power of collaboration and how combining strengths can lead to greater success and market impact.

8. The Leadership Journey Story

• Beginning: Share the background of a leader in the company, including their early experiences and what shaped their leadership style.

• Middle: Narrate key moments in their career, especially those involving significant decisions, challenges, and achievements within the company.

• End: Reflect on the leader’s impact on the company, their philosophy, and the legacy they are building or have built.

Purpose: Leadership journey stories humanize company executives, showcasing their personal growth, values, and the direct influence they have on the company’s culture and success.

9. The Industry Insight Story

• Beginning: Present an overview of the industry, focusing on a lesser-known fact or trend that is shaping its future.

• Middle: Delve into the insights or data that reveal this trend, explaining its implications for businesses and consumers alike.

• End: Offer a perspective on how to leverage these insights for strategic advantage, positioning the company as a thought leader.

Purpose: Industry insight stories establish the company as an expert in its field, providing valuable information that positions them as a go-to resource for understanding industry trends.

10. The Future Forecast Story

• Beginning: Describe the current state of the industry or market and the emerging trends or technologies on the horizon.

• Middle: Predict how these trends will evolve and their potential impact on the industry and society.

• End: Discuss how the company is preparing for or leading the way towards this future, with a call to action for the audience to join or support their journey.

Purpose: Future forecast stories position the company as a visionary leader, capable of not only predicting but also shaping the future of the industry.

11. The Contrarian Story

• Beginning: Present a commonly held belief or practice in the industry.

• Middle: Challenge this norm by introducing an alternative perspective or approach, providing evidence and reasoning.

• End: Show the success or potential of this contrarian view, illustrating how it leads to innovative solutions or improvements.

Purpose: This type of story is used to differentiate the company as a leader that questions the status quo and is not afraid to take unconventional paths to achieve better outcomes.

12. The ‘What If’ Scenario Story

• Beginning: Paint a picture of the current industry landscape or a typical situation faced by the company or its customers.

• Middle: Ask “What if?” and explore a hypothetical scenario that changes an aspect of this landscape or situation, detailing the potential ripple effects.

• End: Discuss how the company is prepared to address this scenario or how its products/services can lead to a desirable outcome in this alternate reality.

Purpose: This story type sparks imagination and helps the audience consider new possibilities, showing how the company is forward-thinking and adaptable.

13. The Behind-the-Scenes Story

• Beginning: Introduce a project, product, or service from the public’s perspective.

• Middle: Pull back the curtain to reveal the unexpected, often hidden processes, challenges, or innovations involved in its creation.

• End: Highlight the dedication, ingenuity, and values that drive the company, as evidenced by these behind-the-scenes efforts.

Purpose: These stories build trust and appreciation by showing transparency and the hard work that goes into the company's offerings.

14. The Parallel Universe Story

• Beginning: Describe a historical or fictional scenario that parallels a current industry situation or company challenge.

• Middle: Draw comparisons between the two, highlighting similarities in challenges, decisions, and actions.

• End: Conclude with key takeaways or lessons from the parallel scenario that apply to the present context, offering insights or solutions.

Purpose: This creative storytelling approach provides fresh perspectives and insights, helping the audience draw connections and understand complex ideas in a relatable way.

15. The Role Reversal Story

• Beginning: Normally, stories highlight the company’s solutions from its own perspective. Start instead from the viewpoint of a customer, competitor, or even an inanimate object.

• Middle: Narrate the journey or experience from this alternative perspective, focusing on interactions with the company or its products/services.

• End: Reveal insights or outcomes from this unique viewpoint, highlighting how it leads to a deeper understanding or appreciation of the company’s role and impact.

Purpose: To cultivate empathy and a deeper understanding by viewing the company's impact through the eyes of those directly affected by its products or decisions.

16. The Cross-Industry Analogy Story

• Beginning: Begin by discussing a well-known success story or innovative practice from a completely different industry.

• Middle: Draw parallels between that industry’s situation and your own, highlighting similar challenges or opportunities.

• End: Conclude with how the lessons from the other industry can be applied to yours, providing fresh insights and strategies.

Purpose: To foster innovation and strategic thinking by drawing lessons from unrelated industries. This can lead to breakthrough ideas and solutions that competitors in one’s own industry might overlook.

17. The Underdog Story

• Beginning: Start with the humble beginnings or early struggles of your company or a key employee.

• Middle: Detail the journey of overcoming adversity, skepticism, or competition against larger, more established entities.

• End: End with the triumph, focusing on the grit, determination, and unique strengths that led to success.

Purpose: To inspire and motivate both internal stakeholders and external audiences by showcasing the company's resilience and determination.

18. The Time Travel Story

• Beginning: Begin in the future, describing an ideal scenario or the company’s long-term vision as if it has already been achieved.

• Middle: Travel back to the present, outlining the steps and strategies being implemented to reach that future state.

• End: End by connecting the future and present, emphasizing the foresight and innovative actions taken now to achieve the desired outcome.

Purpose: To emphasize the company's forward-thinking and long-term planning capabilities. It reassures stakeholders of the company’s preparedness for future challenges and its commitment to a vision that is steadily coming to fruition.

19. The Myth-Busting Story

• Beginning: Start by identifying a common myth or misconception in your industry.

• Middle: Use data, anecdotes, and analysis to debunk the myth, providing a new perspective or clarifying misunderstandings.

• End: Conclude with how your company or product provides solutions based on the corrected understanding, positioning yourselves as informed and trustworthy experts.

Purpose: To establish the company as a thought leader and a credible source of information by correcting misconceptions and providing accurate insights.

20. The Legacy Story

• Beginning: Begin with the long-term vision or mission of the company, focusing on the lasting impact it aims to have on the industry, society, or the world.

• Middle: Detail the foundational values and strategic actions that guide the company toward achieving this legacy.

• End: End by showing the progress made so far and the roadmap for continuing to build this legacy, inviting others to be part of the journey.

Purpose: To establish the company as a thought leader and a credible source of information by correcting misconceptions and providing accurate insights.

21. The Cultural Shift Story

• Beginning: Start with the traditional culture or practices of the industry or company.

• Middle: Discuss a pivotal moment or realization that led to a significant shift in values, priorities, or operations.

• End: Conclude with the positive outcomes of this shift, such as increased innovation, improved employee satisfaction, or greater social responsibility.

Purpose: To illustrate the company's adaptability and commitment to continuous improvement.

22. The Eureka Moment Story

• Beginning: Begin with the backstory of a challenging problem or a stagnant situation.

• Middle: Narrate the moment of sudden insight or discovery that led to a breakthrough solution or innovation.

• End: End with the implementation of this solution and its transformative impact on the company or industry.

Purpose: To celebrate creativity and momentary inspiration that lead to significant breakthroughs.

23. The Ripple Effect Story

• Beginning: Start with a small, seemingly insignificant action or decision made by the company or an individual within it.

• Middle: Detail how this action set off a chain reaction, leading to significant, widespread changes or results.

• End: Conclude with the major impact of these changes, illustrating the power of small beginnings.

Purpose: To demonstrate the power of small actions and decisions and their potential to create significant outcomes.

24. The Symbiotic Relationship Story

• Beginning: Begin with two or more entities (e.g., companies, departments, or organizations) that appear unrelated or competitive.

• Middle: Describe how they found common ground or a way to collaborate for mutual benefit, emphasizing the process of building the relationship.

• End: Conclude with the successful outcomes of this collaboration, highlighting the value of partnerships and synergy.

Purpose: To showcase the value of collaboration and strategic partnerships. This story emphasizes how alliances between seemingly disparate entities can unlock new opportunities and mutual benefits, enhancing each partner's strengths.

25. The Renaissance Story

• Beginning: Start with a period of decline or stagnation, either within the company or in the industry.

• Middle: Detail the transformative actions taken to revitalize and innovate, such as adopting new technologies, restructuring, or rebranding.

• End: End with the company or industry experiencing a renaissance, thriving and leading the way forward.

Purpose: To convey a message of resilience and renewal. It highlights the company's ability to reinvent itself and thrive amidst challenges, inspiring confidence and a forward-looking attitude among stakeholders.

26. The Parallel Worlds Story

Beginning: Describe two different industries or markets as separate worlds with unique challenges and successes.

• Middle: Explore how an innovation or strategy from one ‘world’ can be adapted to solve a problem in the other.

• End: Showcase the surprising success of this cross-pollination, emphasizing the power of unconventional thinking.

Purpose: To foster innovative thinking by drawing solutions from outside one's immediate context. This story promotes the idea that cross-industry learning can lead to unconventional and effective solutions.

27. The Full Circle Story

• Beginning: Start with the company’s original mission or founding values.

• Middle: Narrate the journey, including diversions or evolutions from the initial path.

• End: Show how the company comes back to its roots, but with new insights and strengths, completing a full circle.

Purpose: To reflect on the company’s journey, emphasizing the enduring relevance of its foundational values and mission.

28. The Secret Ingredient Story

• Beginning: Present a common challenge or goal in the industry.

• Middle: Reveal a unique approach or ‘secret ingredient’ your company uses to address this challenge.

• End: Illustrate the effectiveness of this unique approach, making it a coveted or admired aspect of your brand.

Purpose: To highlight a unique factor or approach that sets the company apart from competitors. This

29. The Unsung Hero Story

• Beginning: Highlight a person or team within your company who typically works behind the scenes.

• Middle: Describe their contributions, challenges, and the impact of their work on the company’s success.

• End: Celebrate their achievements and underscore the value of every team member’s role, promoting a culture of appreciation and inclusivity.

Purpose: To recognize and celebrate the contributions of individuals or teams that are crucial but not usually in the spotlight.

30. The Quantum Leap Story

• Beginning: Describe a period of slow progress or stagnation.

• Middle: Introduce a radical change or discovery that led to a sudden, significant advancement.

• End: Detail the new level of achievement or understanding, emphasizing the transformative power of bold actions.

Purpose: To highlight the company's capacity for breakthrough innovation that dramatically advances its position or understanding in the industry.

31. The Domino Effect Story

• Beginning: Start with a small, strategic change or decision made by the company.

• Middle: Trace the cascading effects of this decision through different aspects of the business and beyond.

• End: Highlight the extensive impact, showing how small shifts can lead to substantial outcomes.

Purpose: To demonstrate how small, strategic decisions can set off a chain reaction that leads to significant, company-wide or even industry-wide changes.

32. The Time Capsule Story

• Beginning: Imagine sealing a ‘time capsule’ with elements representing the company today.

• Middle: Describe what these elements are and why they were chosen, reflecting the company’s current state and values.

• End: Project into the future when the capsule is opened, exploring how the company's present will be perceived and the legacy it will have left.

Purpose: To reflect on the company's current identity and values, projecting how these will be perceived in the future and their lasting impact.

33. The Metamorphosis Story

• Beginning: Outline the original state or form of a product, service, or the company itself.

• Middle: Detail the transformation process, highlighting significant milestones, challenges, and innovations.

• End: Reveal the new state or form, emphasizing the evolution and improvements made.

Purpose: To illustrate the company’s ability to evolve and transform significantly, improving its offerings and adapting to changing environments.

34. The Lost and Found Story

• Beginning: Describe something valuable that was lost or forgotten by the company or industry (e.g., a core value, mission, or innovative spirit).

• Middle: Narrate the journey of rediscovery, including the challenges faced and lessons learned along the way.

• End: Showcase the positive impact of reclaiming this lost element, highlighting its renewed importance and contribution to success.

Purpose: To underscore the importance of maintaining core values and missions, showing how rediscovering and embracing them can lead to renewed success and relevance.

35. The Experimental Lab Story

• Beginning: Present the company as a laboratory, where experimentation and innovation are the norms.

• Middle: Describe various ‘experiments’ or projects undertaken, emphasizing the creative process and willingness to take risks.

• End: Share the successes (and failures) of these experiments, illustrating how they contribute to learning, growth, and eventual breakthroughs.

Purpose: To showcase the company's dedication to innovation and learning, emphasizing how a culture of experimentation can drive growth and new discoveries.

36. The Gateway Story

• Beginning: Introduce a seemingly small or simple product or feature developed by the company.

• Middle: Expand on how this feature opened up a host of unexpected opportunities or markets.

• End: Highlight the transformative impact this gateway has had on the company’s growth and industry position.

Purpose: To demonstrate how small innovations can lead to large-scale transformations, emphasizing strategic thinking.

37. The Feedback Loop Story

• Beginning: Describe an initial product launch and the mixed feedback received from the first users.

• Middle: Detail how the company used this feedback to refine and improve the product iteratively.

• End: Conclude with the relaunch or update of the product, showcasing its success and the satisfaction of users.

Purpose: This story illustrates the value of customer feedback in driving product perfection and customer satisfaction.

38. The Global Connection Story

• Beginning: Start with the challenge of entering a new, culturally different market.

• Middle: Describe the learning curve and adaptations made to respect and meet local needs and preferences.

• End: Showcase the successful integration into the market and the mutual benefits gained.

Purpose: To highlight the company's global reach and cultural sensitivity, underscoring its commitment to thoughtful expansion.

39. The Resurgence Story

• Beginning: Paint a picture of a period of downturn or crisis for the company.

• Middle: Narrate the strategic decisions and leadership actions that revitalized the company.

• End: Celebrate the company’s return to strength, better than before.

Purpose: This story reinforces themes of resilience and proactive management, inspiring confidence in the company’s stability and leadership.

40. The Unexpected Hero Story

• Beginning: Focus on an overlooked or underappreciated aspect of the business, such as a back-office function or a junior team.

• Middle: Describe how a crisis or opportunity revealed the crucial importance of this overlooked aspect.

• End: Highlight the newfound appreciation and improvements made to recognize and enhance this area.

Purpose: To encourage recognizing and valuing every part of the organization, promoting a holistic approach to business success.

41. The Green Revolution Story

• Beginning: Start with the environmental challenges and pressures facing the industry.

• Middle: Discuss the company’s initiatives and innovations in sustainability.

• End: Show the environmental and business benefits achieved, setting a benchmark in the industry.

Purpose: To showcase the company’s commitment to sustainability and its role as an environmental leader.

42. The Second Chance Story

• Beginning: Introduce a failed product or project and the lessons learned.

• Middle: Detail how these lessons led to a new approach or innovation.

• End: Celebrate the success of the new venture that wouldn’t have been possible without the initial failure.

• Purpose: This story promotes a culture of learning from mistakes and viewing failures as stepping stones to success.

43. The Harmony Story

• Beginning: Outline a significant conflict within the company or with a partner.

• Middle: Describe the steps taken to address and resolve the conflict, focusing on communication and collaboration.

• End: Highlight the stronger relationship and better outcomes achieved through this resolution.

Purpose: To emphasize the importance of conflict resolution and the value of working together towards common goals.

44. The Pioneer Story

• Beginning: Describe a daunting challenge or uncharted territory in the industry.

• Middle: Narrate how the company approached this challenge with pioneering strategies or technologies.

• End: Showcase the leadership position now held in the industry thanks to these efforts.

Purpose: To highlight the company’s role as an innovator and leader, paving the way for others.

45. The Legacy Project Story

• Beginning: Start with the vision of creating a long-term, impactful project that goes beyond typical business goals.

• Middle: Detail the development and challenges of the project, emphasizing its scale and significance.

• End: Reflect on the project’s impact, both internally on the company culture and externally on the community or industry.

Purpose: To demonstrate the company’s commitment to leaving a lasting, positive legacy, aligning business success with broader societal benefits.

I spent over three years at Google, contributing to the creation of several career certificates, including the Digital Marketing and E-commerce Certificate, where I took on roles as a writer, subject matter expert (SME), and content manager.

During my time at Google, I gained numerous valuable insights—five of which stand out:

1. Be Speedy (respond quickly)

I believe I heard on a podcast, or possibly read in a book, that the highest performing founders are typically the ones that respond the quickest.

For example, if an investor sends them an email, they respond quickly. Much more quickly than you anticipated they would. 

I noticed the speedy response from the Google employees I worked with. The Program Managers responded exceptionally fast to requests. The channel could have been chat or email or within docs. Whatever it was, they were speedy. 

How did that quickness affect me?

Early on in the project, I decided that if they were responding fast, then I needed to respond fast too. I would silence/ignore chat and email when doing deep work. However, when not doing deep work, I was very available on chat and email. I tried to reply as quickly as possible. 

My takeaway is high performers respond fast. Of course, this isn't 100% always true. But in general, yes, it is true. 

Speediness has many benefits for a team. One benefit I noticed, especially important in a startup-like environment, or really, anyone creating something difficult, a lot of decisions need to be made and leaders need to provide direction. Making decisions (quality, informed decisions) quickly helps keep the project buzzing at a smooth pace.

2. Be Kind (empathy required)

Let's go wayy back to the initial interview. 

The interview was with a group of eight people. During the interview, I asked the project lead what type of skills they needed for the project and what they were looking for in potential candidates? 

I was expecting a response stating a specific skill or software experience. Her response surprised me greatly.

She paused, took a deep breath, and said, "What we are looking for is...kindness."

Kindness? What a surprising response! 

With all these intelligent, hardworking people at Google, you'd think she wants more of that, right? We're Google, we're doing big things, we need talent talent talent.

But, no, the response, wasn't a skill or a talent, but a softer response, kindness.

I found that response interesting because in my mind, aren't all employees kind? Isn't that a requisite for working long-term in a company?

It had been around eight years since I worked in a larger, corporate environment. I forgot that often people get promoted on analytics and performance. Not every time, but I noticed that by promoting through mainly performance can put people in management positions requiring people and social skills they don't have.

I'm not saying that's what happened at Google. And to be clear, everyone at Google I worked with was exceptionally kind and considerate of others when providing information. 

I always appreciated the "pre-suasion" involved when providing information. We'll have to cover pre-suasion in another article. The gist of it is what you say before the info is just as important as the info itself. 

So, if someone was delivering negative information, they put consideration into framing the information positively before providing it. That takes empathy and kindness.

3. Wrangle chaos into organized chaos

The projects I worked on at Google resembled a startup environment, particularly when I first started and there were few systems in place. Work structures and style guidelines changed almost daily as we tried new things and learned as we built.

I really enjoy that type of changing environment. I like to stay flexible and open to change. 

Being open to change is a concept you learn in improv. Whatever you think may happen changes in an instant based on what another person may say. 

Here's an analogy I've learned in improv of someone being able to adapt to change. As new information comes in, they're like a tall sandbag that gets pushed around. The sandbag may move from one direction to the other, but because it has a strong base, it doesn't fall over.

The shifting sandbag is more immediate in improv than in a startup, but the principle still applies.

I enjoy the shifts.

I appreciated how the PgMs and IDs at Google shielded the team from much of the upper-level chaos. By the time information or direction came down to us, it was typically well organized and communicated clearly. Sometimes changes were made quickly with our input. 

The environment was very much chaotic but organized. The projects could have easily been more chaotic. Trackers and docs were always created to try and keep the chaos organized. 

I didn't fully appreciate at the time the importance of planning and appearing to have the chaos under control. But I do know now that clear documentation and communication is a way to lasso and tame the chaos.

4. Don't micro-manage but do check-in

My most productive period of content creation came when the Program Manager implemented a simple way to keep track of what we were working on.

When I first started, contractors had weekly assignments and needed to check in at the end of the week. Typically, we selected drop-downs on a custom tracker. 

This issue was some contractors weren't getting their work done within the week. So, to better understand what work was completed when the Program Manager had us email her at the end of every day about what we accomplished. A sentence summary was fine.

This simple check-in really got me focused on the project quickly. It also felt good to communicate what I got completed at the end of the day. 

Yes, I could keep my own to-do list, or update the tracker, but the act of communicating to another person felt more real. 

After writing the summary for a couple weeks, I decided to make a small alteration to my summary.

In addition to what work I completed, I also wrote what I would do the next day in a sentence. 

A very simple change. The clarity of framing the work I was going to do. 

One aspect I think the PM did well was to respond positively to the email at the end of every day. I think that added a human aspect to the emails and also since I know she was reading the email, accountability from me.

While not measurable, I think the positive response provided a quick dopamine hit. Receiving positive feedback every day does feel good. 

Maybe, eventually, the positive response would lose its luster and I wouldn't feel a benefit after a year. But when we're working hard on a chaotic project for three months, it worked. If she just read the email and didn't respond, I don't think the check-in would have been as effective. 

Additionally, because this feedback happened early in the project, it made me feel more secure in the role rather than the uncertainty of not knowing if I was living up to the expectations of the project. 

I think this confirmation is extra important when there's a threat of losing a job. If someone learns that contracts are being ended early, or if you're replacing a vendor that didn't work out, the employee needs additional positive feedback.

People in many ways aren't complicated. We do like frequent positive feedback.

Maybe you're thinking that a daily email check-in sounds a little micro-managey. I don't think so, and it didn't feel that way, especially before trust is built up that you're going to do your tasks.

5. Encourage "Open Communication"

When collaborating on content, an important mindset I picked up was using open communication instead of closed communication. 

It's difficult to put into words because it's somewhat of an intuitive feeling. I'll try to explain with an example:

Let's say I'm collaborating on a piece of content and offering a suggestion. 

Instead of directly stating the suggestion, I'd use a phrase such as "My first thought is..."

This phrase opens dialogue because it indicates that it's more of a first-draft thought than something I'm fully attached to.

It's my first thought but that thought can change!

Another open communication strategy was to make the suggestion less about me and more about the end user. For example:

"xyz would be best for the user."

Rather than,

"I think xyz is the best choice."

The wording is a subtle change, but making the recommendation more about the user rather than the person making the suggestion, places less emphasis on the ego.

The ego likes closed communication. I think people speak from the ego without even knowing it. It's a subconscious process.

Focusing on the end user subtly lessens the ego from the suggestion.

If every contributor writes from the ego, closed communication happens. In writing and content, people can often get attached to their writing. This attachment can lead to closed rather than open communication.

Wrap-up

Overall, I thoroughly enjoyed my time at Google. I don't think I could have been treated better as a contractor and I'd certainly work there again in the future.

After all this experience, why do I hesitate to give myself a higher grade? The hesitation primarily stems from my encounters with super prompts. You may have seen them: paragraphs and paragraphs of long, elaborate instructions. The kind of prompt that took days to perfect. While super prompts impress me with their complexity, they don't excite me.

If super prompts are the future of gen AI, then I'm certainly behind. I much prefer to work with the AI as a step-by-step assistant.

Crafting Dialogue with AI: Beyond Super Prompts

While I think super prompts can be helpful for more complex data analysis, I much prefer to interact with the LLM model like a chatbot assistant: a back and forth experience working through one problem one step at a time. I'm not drawn towards the idea of crafting and working with the 'one prompt to rule them all'.

I asked ChatGPT for an example of a very long and detailed prompt. Here's what it provided (I'd consider this a mini super prompt):

Rather than crafting super prompts, I'm inclined to work with ChatGPT as I would with a person. Reading Ethan Mollick's posts on his blog, One Useful Thing, has shaped my mindset. What I've gleaned from his content is to consider the LLM more so a friendly assistant rather than a software.

For example, in the prompt above, I'd first create an outline. I'd go through the outline, working with the LLM model step-by-step. For me, this process brings me closer to my desired finished product. I've also found that the step-by-step method allows me to learn as well. And as I learn, I may alter the future prompts.

LLMs are fickle. Depending on the topic or task, it may act different in subtle ways. I like to learn those subtleties as I work with it. When I identify the subtlety, I work around it. A simple example is word choice. This is how altering word choice may play out: You assign the LLM a certain role, an expert in something. As an expert in that field, it may use words common to that field and repeat those words in outputs. Those words may be the wrong tone. So you work with the LLM to avoid those types of words.

When using a larger prompt without input along the way, I've found the output more closely matches the LLM's preferences than mine. That can be frustrating.

I prefer learning alongside the model, rather than from a vast block of text.. That takes a different skill. You have to know and understand the LLM. How the model works. Its limitations and strengths. Which takes time.

Additionally, I find the experience of talking like an assistant closer to my personality. I'm not a programmer. It's a struggle for me to think like a programmer (I'm still working on it).

I'd rather have a relationship with the LLM. A back and forth like a team member. Weird, right? Maybe that's because I work from home and could use the company. I'll admit that.

The AI Conundrum: More Than Software, Less Than Human

One mindset I learned from reading the articles in One Useful Thing is to not think of the LLM as a software. Unlike software, it doesn't have perfect outputs, it makes mistakes, it can hallucinate or produce inaccuracies, and often forget instructions. The longer your conversation with the LLM, the most likely it will forget your previous request. For longer convos, I have to remind it of something I previously stated.

When it's forgetful, I have to correct it as I would a novice employee. Except, it doesn't have feelings (...yet) so I can be more direct with the changes. I like that.

I will say, it's an interesting experience to correct an LLM. For example, "Hey, aren't you supposed to ask me one question at a time instead of all the questions at once?" Then, it often apologizes and does what you originally requested.

So, while it's not exactly like software and sometimes requires treatment similar to a person, it's obviously not a person. As of publishing this post, the LLMs still lack, what I would call, the "self awareness" a person may have, and that's because it's still a software.

I'd define self awareness as its ability to observe its output. I've found that LLMs can be too trigger happy to provide an output when instead it should ask more questions to better understand my intent. When it receives little input, the output is unlikely to match what I'm looking for. I get frustrated, and beginner users who are likely to provide the short prompts, get frustrated.

Something I'm curious about for the future: How much self-awareness will GPT-5 have? I've read about solutions to bring more precision and self-awareness, such as using multiple agents to interact with each other. Having multiple agents work together to create an output could possibly give the illusion of this self-awareness.

Overall, with every new model upgrade, the LLM appears to become more and more like a person, so I believe the self-awareness will increase.

Embracing AI as a Collaborative Partner

Building on the idea of the LLM model being like a team member, I'll share a unique experience that I had and I've read others have had as well. This experience created a paradigm shift for me.

A few factors facilitated this paradigm shift. One, I used the LLM extensively on a project. When submitted, this project received a lot of praise. One of the pieces of praise was how much information was included. The last factor was me knowing that couldn't have done as good of a job if I didn't use ChatGPT. Using the software, I created something of higher quality and output than I could have achieved alone. Incredible really.

Here's an example of my project workflow with ChatGPT:

• ChatGPT - Create an outline

• ChatGPT - Create a second, more diverse outline

• Me - Rework the outline myself

• ChatGPT - Write initial paragraphs as inspiration

• Me - Rework the paragraphs, often significantly

• ChatGPT - Recommend edits to my work

• Me - Decided to whether to implement edits or not

• ChatGPT (throughout)- Brainstorm ideas when I was blank

Over a span of several days, I worked closely with ChatGPT to complete the entire project. I had an output of probably twice than if I would have done it on my own. In addition to the work output, I did it all in probably half the time.

The workflow was a bit clunky because it was my first time using ChatGPT for this type of project and for the topic. But, if I had to do it again, it could have been completed much more quickly.

Navigating the Prompting Maze: A Beginner's Journey

Prompting, as of now, isn't easy for a beginner to understand. It's a completely new skill. And really, prompting isn't the right word. As LLMs become more intelligent, we'll be "working" with the AI, not prompting it.

For a beginner, I do like the recommendation to work with it for 10 hours before making any judgements on a gen AI's ability.

After those 10 hours, your opinion and understanding of the software/person operating in the LLM neural network will surely evolve. You'll have some frustrations. You'll have some suprises.

I foresee the LLM companies creating a "beginner mode", where there is more hand holding with beginner prompters. The beginner mode will ask the user more questions to better understand the type of output they would like instead of relying on the user to figure it out.

Additionally, I foresee companies implementing yearly training for their employees to work better with LLMs. Even minor training could improve outputs drastically.

I have several more tech-savvy friends who use ChatGPT to complete their work. Their less-tech savvy peers and superiors are impressed by the quality and diversity of their work. However, they don't share that they're using AI! And why tell them? It's better to just keep it secret and impress!

Personalizing AI Interaction: My Conversational Approach

Typically, my approach to content creation with an LLM is conversational. I prefer to chunk content and work with the LLM in pieces rather than to work in multiple paragraphs or one piece.

If the paragraphs are shorter, then I'll work within an idea. Taking several short paragraphs, maybe a sentence long, and combining to create a point.

Overall, I enjoy using the LLM to solve specific writing problems rather than writing entire pieces. This process may expand past a paragraph into two paragraphs. For example, if I'd like a better transition between paragraphs, I will explain the writing problem I'd like to solve (I think the transition is clunky and could be more smooth), the solution (provide a few more transition ideas rather than xyz), and then the output (provide the two paragraphs).

And often, the longer the chat convo, other content issues pop up. This is because the AI's has a poor memory. An instruction you gave it a few messages ago is something they forget. It's like LLM wack-a-mole. The deeper, you go into the thread, the more issues may pop up.

I imagine the LLM companies are working on this and ideally, the LLM will be self-aware of your instructions. For example, it may ask if you want it to continue following your initial instructions and why changing the instructions may be more beneficial for your desired goal.

Envisioning the Future: AI That Understands Us

Interacting with LLMs and the idea of prompting will change in the future. How will it change, no one knows for sure. I think the model's memory will certainly improve. And when the memory improves, so will the precision. I also think it will be more self aware. There will be more effort in the LLM processing before the output to better understand the intent of the user. Which may come in the form of asking questions when it's confused.

Much of the content I've encountered on LLM usage is geared towards programmers, rather than the gen pop. One of the reasons why I enjoy reading Ethan Mollick's pov is because he's working with students who are using the LLMs with their school work. In addition to his first hand knowledge with prompting, he's observing others attempt to prompt as well. He provides more of a non-programmer perspective.

In the future, the money will be in which model is most user-friendly. Which model can a less tech-savvy individual start using and have it improve their workflow. Which model is a pleasure to work with, rather than a frustration.

I imagine OpenAI is working towards the future with a more gen-pop friendly LLM. And I certainly look forward to what gpt-5 will be capable of.

I was learning more about screenplay storytelling and I stumbled onto a video of an indie filmmaker talking about using ChatGPT to write a script.

He said he just finished writing the first draft of a script. This first draft would typically take him six weeks with a co-writer to write, but with ChatGPT he was able to get a beat sheet and dialogue cranked out in a day. A day!?

I was able to find the video!

Once I heard him say this, I had to learn more about this generative AI technology and how it would aid in creating. This launched me into a 6-month deep dive into machine learning, python, and gen AI.

Here are a handful of takeaways from my 6-month deep dive, with an emphasis on how I think it will affect online learning:

Generative AI is better at teaching a concept than a person.

What shocked me, even with ChatGPT-3.5 was the clarity in which it would explain a concept. The LLM produced an explanation better than most websites. And unlike a person, it didn't need rewrites, it didn't need an editor. Plus, I could ask it to explain the concept in different ways, such as if I was in 5th grade. I could ask it for examples. Even better, I could ask it for examples of a specific industry.

My first experience of gen AI explaining concepts better than the teacher was during an intro Python course on Coursera. Earlier in the course, I was getting confused by the teacher's exemplar. It just didn't seem clear. I literally copied and pasted the question into ChatGPT and not only spit out the correct answer, but the code comments were so much more clear than the instructor I was actually able to understand the coding concept. Then and there I knew teaching was going to change. I went on to use ChatGPT to help explain almost every exercise.

Gen AI is neither a software nor a person. It's something in between.

The concept of "something in between" is difficult for people and companies to understand. Gen AI isn't software, it often makes mistakes. On the other hand, it also isn't a person, it can't understand context like a person does. Gen AI lands somewhere in between.

When working with gen AI, you can speak to it like a person and it will for the most part understand. However, you'll have to coach it and massage its outputs to get the result you want. This is a skill that only comes with a lot of time working with the LLM. At least 10 hours, ideally over 100.

Gen AI is a tool, rather than a complete replacement.

In learning, we won't be able to use LLMs to craft the entire curriculum. Currently, what is called the LLMs' "context window" is too limited. For example, we can use it as a tool to draft syllabi (similar to the beat sheet in the video above), and specific sections, such as lessons in curriculum. Beyond that, the LLM likely won't create specific curriculum for what we need.

However, keep in mind, that one of the reasons why LLMs are limited in their ability to craft more complex items, such as in-depth curriculum for a six-week course, is because of the limited context window. Google just released an advanced version of Gemini with the ability to hold around 750,000 words. Which is a total game-changer.

Very soon the general public may be able to prompt long, detailed instructions to the gen AI so that the output is much more specific to what we want. Additionally, soon, the gen AI will be able to create images and diagrams to supplement the curriculum, which will be absolutely helpful (and crazy to consider the cost savings) from the learning perspective.

One writer will be able to do the work of three writers and an editor.

One writer who can prompt well is able to do the work of several writers.

Now, that doesn't mean we should use LLMs for all our writing. For me, writing is a process of understanding. To get the jumbled-up thoughts in my head down on paper. And after writing, the ideas are better explained verbally.

While working with an LLM does help me understand a topic better, particularly how to explain a topic concisely, it doesn't help me organize my own thoughts like writing does.

If we still want people who can explain a topic well in a classroom, a training, or even through a video platform like TikTok, the process of writing is still necessary. Clear writing = clear thinking.

A writer who knows how to work well with gen AI won't need an editor

I've worked with editors for a few years and I think if you prompt the LLM correctly, it can come over close to replacing an editor. And I think the software and pre-training for this task will get there in the next couple of years.

We will still need a position to ensure that the writing adheres to the style guide and is factually accurate. However, can I diligent writer do that? Additionally, the better a writer can prompt an LLM, the closer they will be able to get the writing to the style guide.

Additionally, the large context window, aka more information, to provide the LLM may help it better understand the role it needs to play and the end result you would like to achieve with the writing.

The future of the writer role will certainly change

When the more advanced abilities of gen AI were released, many people were saying that writers will eventually become prompt engineers. Even if the LLM technology advances significantly, I don't think that's entirely true.

It's difficult for LLMs to fully understand the context of what you're trying to write. They're good at bland, unoriginal content. Yes, that writing can be prompted to be more creative, but it often misses the mark on the tone and purpose. Sometimes the output is fun and better than you imagined. However, often for me, I've gotten frustrated with the output.

So, I think the future role of the writer will be more of a coach and editor. You'll need to know how to communicate to the LLM as a coach to rework the writing.

Most importantly, and I think the largest takeaway for me, is that writers will need to keep their "noticing" ability. They still need the ability to notice good writing from bad. And when the LLM creates content they were originally looking for. At least for now, we need people to have the skillset to do the noticing.