Why Your AI Writing Sounds Fake (And How to Fix It)

How to make AI writing sound like you?

You know that feeling when you read something and immediately know it was written by an AI?

Not because it's wrong. Not because it has bad information. Just because something feels... off. Corporate. Soulless. Like it was generated by someone who read a Wikipedia article about emotion and tried to replicate it.

That's what most AI writing sounds like.

And here's the thing: it's not the AI's fault. It's yours. Or more specifically, it's what you're asking the AI to do.

Most people use AI like a vending machine. Drop in a prompt, get out content. Then they're shocked when it sounds like every other AI-generated thing on the internet. Because it kind of is. Same tool, same prompts, same training data, same output.

We stopped doing that years ago.

TL;DR

• AI defaults to bland corporate language because it mirrors the average writing in its training data, not the sharp stuff your brand needs.
• Treat the model like an assistant: feed it your voice, supply examples, set hard constraints, and build sections iteratively instead of one-shot prompts.
• Let humans set headlines, strategy, and editing standards while AI handles research, tightening, and polish.
• Publish only after ruthless edits and a read-aloud pass so the final draft sounds like you, not a chatbot.

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Why AI Writing Goes So Wrong (The Pattern Everyone Misses)

There's a reason AI-generated content has a specific flavor. And once you see it, you can't unsee it.

AI models are trained on billions of examples of human writing. But they're not trained on good writing. They're trained on average writing. The stuff that shows up most often. The patterns that repeat constantly.

This means AI gravitates toward:

Corporate language that sounds safe. "Leverage your potential." "Optimize your workflow." "Drive meaningful engagement." These phrases appear millions of times in training data. So the model learns: this is how professionals talk.

But professionals who actually know what they're doing? They don't talk like that. They talk like humans.

Generic adjectives stacked like pancakes. "Revolutionary new solutions." "Cutting-edge strategies." "Seamless integration." The model sees these combinations thousands of times, so it assumes they go together. Real writing doesn't work that way. Real writing is specific. Boring, sometimes. But specific.

Sentence structures that repeat. "X plays a crucial role in Y." "By doing X, you can achieve Z." "Not only does A do B, but it also does C." These templates show up constantly in training data. The model learns them as the blueprint for professional communication.

Except they're not. They're just what's most common.

Hedging language that kills credibility. "It could be argued that..." "Some might say..." "In many cases..." The model learns that careful language is smart language. So it piles on the qualifiers. Real expert writing doesn't do this. Real experts sound confident because they actually know something.

This is why so much AI writing sounds like it was written by a corporate HR department in 1997. Because the model is trained on the safest, blandest, most repeated patterns of writing available.

The Real Problem: You're Not Actually Using AI Right

Here's where people mess up: they use AI as a writer when they should be using it as a research tool or an editor.

They ask: "Write me a blog post about social media strategy."

The AI, having no actual thoughts, writes the safest, most generic version of social media strategy that exists. It hits all the expected points. It sounds professional. It's completely forgettable.

Better approach: You write the first draft. It's rough. It has your voice. Then you ask AI to tighten it, cut the fluff, make it punchy.

Or: You have a specific point you want to make. You ask AI to help you find evidence, examples, data. Then you do the actual writing.

The AI works better when it's doing supporting work, not the main work.

What Actually Sounds Fake (Let's Look at Real Examples)

Let me show you what I mean. Here's typical AI writing:

"To maximize your digital presence, it is essential to leverage cutting-edge strategies that optimize your online engagement and drive meaningful results through a comprehensive approach to content distribution."

Why does this sound fake? Count the problems:

1. "Maximize your digital presence" — vague corporate speak
2. "It is essential to" — hedging language
3. "Leverage" — word that only appears in business writing
4. "Cutting-edge strategies" — generic adjective + noun combo
5. "Comprehensive approach" — two words that should never go together

Here's how a human would say the same thing:

"Your content needs to reach the right people. Post consistently, engage with your audience, and track what works. That's it."

Shorter. Clearer. Confident.

The human version sounds real because it doesn't apologize for being simple.

Technique 1: Give AI Your Voice (Before You Use It)

The single biggest mistake: feeding AI a prompt without context.

Better approach: Show the AI how you talk first.

Here's what we do:

We give the AI a sample of our actual writing. Not our best writing, just our normal writing. A few paragraphs from an email or previous article. Then we tell the AI: "Write in this style."

Example:

"Here's how I usually write: I like short sentences. I use contractions. I avoid corporate words. I get straight to the point. I use examples from real situations. I'm a bit sarcastic sometimes but not mean about it."

Then when you ask for content, you add: "Write this in the style I just shared."

Suddenly the output sounds way more human. Not perfect, but close enough that you only need minor tweaks.

This works because you're not asking the AI to guess at tone. You're showing it exactly what you want.

Technique 2: Give Specific Examples, Not Vague Directions

"Write a social media post about productivity" produces garbage.

"Write a social media post like this: 'Most productivity advice is bullshit. You don't need a system. You need less stuff to do. Start by killing 20% of your to-do list. Then let me know what happens.'"

See the difference? The second one actually shows the AI what you want. The voice. The length. The directness. The tone.

When you give specific examples, the AI models what you're showing instead of guessing.

This is huge. The difference between generic output and useful output.

We always give 2-3 examples of what we're going for. Never just a vague direction.

Technique 3: Add Constraints That Force Better Thinking

Most people use AI without limits. Ask for a blog post, get 1500 words of nothing.

We add constraints:

"Write this in 100 words exactly. Use only short sentences. Use zero adverbs. Include one specific example."

Constraints force the AI to be intentional. It can't pad things out. It has to make every word count.

Same thing with tone. Instead of "write in a friendly tone," try: "Write like you're explaining this to a friend in a text message. Use their actual language. Keep sentences under 15 words."

Constraints = better output.

Technique 4: Write the Headline First (Don't Let AI Do This)

Headlines from AI sound like headlines from AI.

"7 Strategies to Boost Your Productivity Today" "10 Ways to Improve Your Social Media Presence" "The Ultimate Guide to Digital Marketing Success"

They're generic because the AI is trying to include keywords and sound professional at the same time. Never works.

You write the headline first. Make it specific. Make it honest. Make it yours.

Then the AI writes to match that headline. It has direction. It knows what it's actually supposed to be about.

Example: You write the headline as "I spent 6 months not checking email. Here's what happened."

Now when you ask the AI to write the body, it actually knows what it's doing. It's not guessing at tone or angle. You set the direction.

Technique 5: Break It Into Sections (Don't Ask for the Whole Thing)

This one's weird but it works.

Instead of asking for a full article, ask for one section at a time. Headline, intro, first section. Review it. Adjust the prompt based on what you got. Then ask for the next section.

This is slower. But the output is way better. Because each section is getting feedback. You're refining the direction as you go instead of asking for everything at once and hoping it lands.

It's how we actually work. Never hand the AI a massive prompt and hope it returns something usable.

Technique 6: Edit Ruthlessly (AI Is Your First Draft, Not Your Final Draft)

This is the one that matters most.

Stop treating AI output like it's done. It's not. It's a first draft. Your job is to edit the hell out of it.

What to cut:

• Every instance of "leveraging," "optimizing," "driving," "facilitating"
• Every generic adjective: "cutting-edge," "powerful," "robust," "seamless"
• Every hedging phrase: "it could be argued," "some might say," "in many cases"
• Every corporate phrase stack: "comprehensive approach," "strategic initiatives," "digital transformation"
• Every instance of "it is" (rewrite to something active)

What to add:

• Specific examples instead of general statements
• Numbers or data that actually prove something
• Your actual opinion instead of safe positions
• Humor or personality if that's your style
• Questions that make people think

The raw AI output might be 70% there. Your editing job is to push it to 95%.

This is why AI content from people who edit it carefully sounds way better than AI content from people who just hit publish. The difference isn't the AI. It's the editor.

Technique 7: Use AI for Iteration, Not Creation

Here's how we actually work:

You have a rough idea. You write 2-3 paragraphs on it. It's messy. It's got weird phrasing. But it has your actual thinking in it.

Then you ask AI: "Here's what I'm trying to say. Help me make this tighter and clearer without changing the meaning."

The AI takes your rough idea and polishes it. Cuts the waste. Keeps the voice. Keeps the point.

This produces way better results than asking AI to write from scratch.

Because you're using AI for what it's actually good at: refining and sharpening. Not for what it sucks at: original thought and authentic voice.

Technique 8: Read It Out Loud Before Publishing

This sounds stupid but it works.

Copy the AI output. Read it out loud. If you stumble, if it feels weird coming out of your mouth, it's fake sounding.

Real writing flows when you read it. Fake writing trips you up.

If you trip, edit it until it doesn't.

This is the fastest way to spot what sounds off without being able to articulate why. Your mouth knows. Listen to it.

What We Actually Do (The Full Process)

Here's how we use AI without it sounding like AI:

1. Write the headline ourselves. It sets the direction. It's specific and honest.
2. Create a voice guide. 2-3 paragraphs of our actual writing as an example.
3. Write 1-2 rough paragraphs ourselves. Just to set the tone and get the ideas down.
4. Ask AI to build on it. With specific constraints, examples, and direction.
5. Edit everything ruthlessly. Cut corporate language. Add specificity. Remove hedging. Keep the voice.
6. Read it out loud. Catch anything that sounds off.
7. Publish once it reads like a human wrote it.

This process takes longer than just hitting "generate." But the output is actually good.

That's the trade-off. Speed versus quality. We pick quality.

Why This Matters for Your Brand

Your writing is the first thing people experience from you. Before they meet you, before they work with you, they read something you wrote.

If it sounds fake, they don't trust you. Doesn't matter how good your ideas are.

If it sounds real, they think: "This person knows what they're talking about. They're not trying to sound smart. They actually are smart."

That difference is huge.

And it's not about being fancy. It's about being honest. Real writing sounds confident because it doesn't need to convince you. Fake writing sounds nervous because it's trying too hard.

The techniques here aren't about making AI sound better. They're about making sure your voice comes through the AI. The AI is just the tool. You're the writer.

One More Thing: Know When Not to Use AI

This is important enough that it gets its own section.

Some writing shouldn't come from AI at all. Apologies. Thank you notes. Anything deeply personal. Anything where your actual voice matters more than speed.

For that stuff, you write it yourself. Full stop.

AI is great for support work, iteration, speed on volume. It's terrible for things that require authentic emotion or genuine voice.

Know the difference. Use the tool for what it's good at. Don't try to AI your way through everything.

The Real Test

Here's how to know if your AI writing actually sounds human:

Show it to someone without telling them an AI helped write it. If they can't tell, you did it right. If they can tell, go back and edit it.

That's the only test that matters.

Not whether it's perfect. Whether it sounds real.

Further reading

• Remember how we talked about tokens and context? That context matters when you're briefing an AI to write. Clearer context = better output.
• This is what we meant when we said treat AI like an employee, not a magic box. Give it direction, feedback, constraints. That's how you get good work.
• The research phase we discussed earlier? This applies here too. Research what good writing in your niche looks like before you ask AI to write it.
• This article goes deeper into the prompt structure. Read this after Article 11 if you want to level up your prompting.
• Here's a real example of how we used these techniques when writing Vispea's content and fanzine pieces.

External resources

• OpenAI: prompt engineering guide
• Anthropic: getting started with the API
• Hemingway Editor