If you’ve used AI for content, you’ve hit the wall where the writing is fine but it just doesn’t sound like you. To customize your LLM voice, you feed the AI real samples of your writing, give it a short style guide of the words you use and avoid, and run tone tests until it clicks. Do those three things and your content still sounds like you, even when you didn’t type every word.
Getting AI to write in your voice takes a little setup, but once it clicks you stop rewriting every draft. The writing can come out a little stiff, a little too polished, or oddly formal when you were going for something that feels like a conversation. The fix is teaching the AI who you are.
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What Does Customizing Your LLM Voice Actually Mean?
LLM stands for large language model, which is just the technical name for the AI tools you’re probably already using, things like ChatGPT or Claude. If tech terms like this trip you up, my plain-English vocabulary guide breaks them down without the jargon. Customizing your LLM voice means shaping how that AI writes so it sounds like you instead of a corporate press release. Casual, warm, a little cheeky, whatever your actual vibe is. The AI can reflect it if you teach it to.
People still want to feel like they’re reading something from you, not a bot. A consistent voice is what makes that happen, whether you wrote it or your AI got a head start on it.
Why Does Your Brand Voice Matter When You Use AI?
Your logo and colors are fine. But your voice, the way you write, the words you use, the rhythm of how you explain things, is what actually makes people feel like they know you. And if you’re using AI to help with blog posts, captions, emails, or product descriptions, that voice has to stay consistent. Otherwise the content feels off and people notice even if they can’t name exactly why.
A well-trained AI keeps everything aligned. When your audience reads something from you, it should feel like you, no matter how it got written.
Use a Celebrity as a Tone Reference (This Actually Works)
This sounds a little silly but hear me out. If you’re struggling to describe your tone to an AI, sometimes it’s easier to point at someone whose vibe you want to borrow. Not copy, just use as a reference point to get the AI in the right direction.
You might try something like:
“Write this like Kristen Bell is explaining it to a friend. Friendly, honest, a little playful.”
“Write this like Morgan Freeman is narrating it. Calm, grounded, confident.”
“Write this like Will Ferrell giving a pep talk. Ridiculous but somehow motivating.”
You’re not trying to sound like them, you’re giving the AI a shortcut to a feeling. Test a few until something clicks and then you can build from there into what’s actually yours.
I put together a free PDF with celebrity tone prompts you can grab and start testing right away.
How to Actually Train Your AI to Sound Like You
Step 1: Feed It Your Own Writing
The best thing you can do is give your AI real examples of how you write. Aim for five to ten strong pieces to start: blog posts, emails, captions that felt very you, even voice memos you’ve transcribed. The more it has to work with, the better it gets at matching your tone. Pick pieces that are strong in voice, use a range of formats, and don’t just dump in one tweet and call it a day.
Step 2: Write Out What Makes Your Voice Yours
Give your AI a style guide, even a simple one. Tell it what words you use, what you avoid, how formal or casual you are, whether you use humor and what kind. Tell it what you never want it to sound like. The more specific you are, the less editing you’ll do on the back end. Once you’ve written one, you can save it as a Claude Code skill so it loads on its own instead of pasting it into every new chat.
Step 3: Test and Adjust as You Go
This isn’t a one-time setup. Your voice shifts as your brand grows, and your AI needs to keep up. Run tone tests regularly. See what’s landing and what still sounds a little off. If you’re already using n8n and Airtable, you can actually log your style notes and prompt preferences in a structured way so nothing gets lost. I’ll be sharing more on that in an upcoming post.
What Goes Wrong When You Train AI on Your Voice?
The most common issue is giving your AI too many mixed examples. If you feed it five completely different writing styles, it’s going to average them out into something that sounds like none of them. Keep your training examples consistent in tone. The other thing that trips people up is skipping the review step entirely. Your AI will wander into weird off-brand territory if nobody’s checking the work. A quick final pass catches it every time.
| Feed your AI this | Skip this |
|---|---|
| Pieces that are strong and consistent in voice | Five wildly different writing styles at once |
| A range of formats (blog posts, emails, captions) | One lone tweet used as your whole sample set |
| A short style guide of words you use and avoid | No guidance, hoping the AI just figures it out |
| A final human review before anything goes live | Publishing straight from the first draft |
Frequently Asked Questions About Training AI to Write in Your Voice
What is an LLM and why does it matter for content creators?
LLM stands for large language model. It’s the technology behind tools like ChatGPT and Claude. For content creators, it matters because these tools can write in your voice if you train them correctly, which means faster content creation without losing what makes your brand feel personal.
How many writing samples do I need to train my AI voice?
More is better, but quality matters more than quantity. Start with five to ten strong pieces that really sound like you across different formats. A mix of blog posts, emails, and social captions works well. Avoid pieces that feel off-brand or like you were writing in a different mode than usual.
Will my AI-generated content sound fake to readers?
It won’t if you train it well and do a final review before anything goes live. The goal is to use AI for a solid first draft, then add your stories, specific details, and personality on top. That combination is what keeps it feeling real.
Can I use n8n to manage my AI voice training?
Yes. You can build workflows in n8n that route writing samples into an Airtable base, log prompt preferences, and keep your style notes organized in one place. It turns what’s usually a scattered process into something you can actually maintain over time.
Do I need to redo this every time I update my AI tool?
Not completely, but you’ll want to re-test when you switch tools or when a tool gets a major update. Keep your style notes and sample library saved somewhere you can access easily. That way retraining is quick instead of starting from scratch.
The short version: customizing your LLM voice comes down to feeding it your real writing, writing down what makes your voice yours, and testing until it sounds right. Do that and AI becomes a head start, not a replacement.
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Related reading
- Stop Pasting Your Style Guide Into Every New Chat, part 1 of Claude Code for Bloggers, on turning the style guide above into a file your AI picks up on its own.
- Create Custom GPTs That Actually Work for You, for a more hands-on way to bake your voice and rules right into a reusable AI assistant.
- Automate Content Creation for the Shiny Object Chaser, for keeping that voice intact once the workflows are doing the drafting.
If this is the kind of stuff you want more of, subscribe below. I’ll be sharing actual n8n workflows and blueprints that go way deeper into this, including how to build systems that keep your AI voice consistent across everything you create. If you want to go deeper on the automation side while you wait, the AI Automation Society is where I hang out to learn n8n and Claude workflows alongside other non-coders. The good stuff is coming.