Your AI Content Sounds Generic Because You Gave It Generic Input

You paste a prompt into ChatGPT. You ask for a blog post about your topic. It gives you 800 words of confident, competent, forgettable prose. You read it back and think: this could have been written by anyone, about anything, for anyone.

That is not a failure of the model. That is a failure of the input.

AI writes to the average of the internet by default. If you feed it a topic and nothing else, it will pull from every generic post ever written on that topic and hand you back the mean. The output will be grammatical. It will hit the expected beats. It will also sound exactly like every other AI-generated post on the same subject, because it is being asked to guess at what you probably wanted from a bag of words the size of the internet.

The fix is not a better prompt. The fix is more of you.

What generic actually means when a model produces it

When you ask a model for a post about, say, cash flow forecasting for a small business, it has no idea whether you run a bakery or a plumbing company. It does not know that your customers pay net 60 and it is killing you. It does not know that you learned this the hard way after almost missing payroll in March. It does not know the specific spreadsheet you built to fix it, or the vocabulary you use with your bookkeeper.

So it does what any writer with no source material does. It writes a coherent, safe, general piece. Tips one through five. Recommendations that would apply to any small business, in any industry, at any stage.

That output is not wrong. It is just interchangeable. And interchangeable content is the exact thing your audience skips.

The two inputs that actually change the output

Two kinds of input reliably move AI writing from generic to specific:

• A recording of you talking about the topic
• Written notes or prior content in your voice

Both do the same job. They give the model raw material it could not have generated on its own. Your phrasing. Your examples. The way you make an argument out loud instead of on the page.

You do not need either input to be polished. You are not writing the final draft. You are handing the model the specifics it needs so it stops guessing.

Voice recordings

A five-minute voice memo of you explaining the topic to a friend beats a two-page brief every time. When you talk, you skip the caveats you would write in. You use the words you actually use. You give examples without pausing to think about whether they are perfect.

Record yourself walking through the topic as if a coworker asked you about it at lunch. Feed the transcript into the model. Tell it to write the post using your framing, your examples, and your phrasing. Do not tell it to make it professional or clean it up. That is where the voice dies.

Written notes and prior content

If you have written on the topic before, even in a Slack message or an email reply, feed that in. Old blog drafts. Notes from a client call. A LinkedIn comment you left last week. Anything where you already sound like you.

The model will pattern-match against it. If your prior writing uses short sentences, it will write shorter sentences. If you tend to open with a specific example instead of a general claim, it will do that.

The more of your prior words the model can see, the less it has to guess.

A concrete before-and-after

Here is what a prompt without your context looks like:

Write a 1000-word blog post about the importance of cash flow forecasting for small businesses.

And here is roughly what comes back: an intro about how in today's competitive market small businesses face pressures, a numbered list of five reasons forecasting matters, a section on tools, and a closing paragraph telling the reader to get started today.

Now compare that to a prompt with your context:

Below is a five-minute transcript of me explaining how I forecast cash flow for my three-person consulting firm. I almost missed payroll in March 2024 and rebuilt my whole system after that. Use my examples, my phrasing, and the specific spreadsheet I describe. Write a 1200-word blog post. My audience is other small-firm owners who are still doing this in their head.

[transcript pasted]

The second version gives the model something no other prompt on the internet has. It cannot fall back on generic small-business advice, because you gave it a specific story, a specific audience, a specific system, and a specific voice. The output will still need editing. It will not still sound like every other post.

What to record or write down before you prompt

You do not need a full outline. You need raw material. Before you open the model, spend ten minutes producing one of these:

• A voice memo where you answer: what is the one thing about this topic that people usually get wrong, and how would you explain it to someone across the table
• A voice memo where you walk through a specific example from your own work, start to finish, with the numbers and names left in
• Three or four bullet points naming the exact objections or questions your audience asks about this topic
• A paragraph you have already written elsewhere (Slack, email, an old draft) that touches the same ground

Any one of these will change the output. Two will change it more.

What the model should be doing with your input

Once you have the raw material, be direct about how you want it used. A few phrasings that work:

• Use my transcript as the spine of the post. Keep my examples and my phrasing. Do not add generic advice I did not include.
• Write in the voice of the attached prior post. Match sentence length and how it opens with a specific example.
• If something in my transcript is unclear, ask me before making it up. Do not fill gaps with generic content.

The last one matters. Left unprompted, models will paper over the missing pieces with the same generic filler you were trying to avoid. Telling it to stop and ask is often the difference between a draft you can edit and a draft you have to rewrite.

Why this feels harder than it should

The reason most people skip this step is that it feels like extra work. You came to the model because you did not want to write the thing. Now the advice is: talk for five minutes, or paste in prior writing, or write bullet points about your audience.

That is still less work than writing the whole post. And it is less work than publishing generic AI output and watching it get ignored. The recording is not the deliverable. The recording is the input that lets the model produce something worth publishing.

If you already have a body of writing (past newsletters, blog posts, client emails), you have a running head start. Keep a folder of your best prior work and paste from it. The library gets more useful every time you add to it.

What good output looks like

When you have given the model enough of you, the output should:

• Include specific examples you would recognize as yours
• Skip the corporate opener and get into the actual point
• Use the vocabulary and cadence of your prior writing
• Leave in the parts of your view that are opinionated or unusual
• Read like a draft you could sharpen in twenty minutes, not rebuild in two hours

If it does not do those things, the fix is more input, not a cleverer prompt. Add another five minutes of recording. Add another prior post. Tell the model what to keep and what to cut.

One thing to try this week

Pick a post you have been meaning to write. Before you prompt anything, open your phone's voice recorder and talk for five minutes about the topic as if you were explaining it to a peer over coffee. Transcribe it. Paste the transcript into your model with one instruction: use my framing, my examples, and my phrasing, and ask me before filling any gaps. Then compare that output to whatever you would have gotten from a topic prompt alone. The gap between those two drafts is the value of your voice.