The idea of having an AI that writes just like you – it sounds like sci-fi, but in 2025 it’s actually within reach. Imagine an AI assistant that has absorbed your past articles, your tone, your quirks, and can draft content in a manner that resembles your style. This could save time by producing decent first drafts or translating your style into different formats. The process of achieving this is essentially training (or fine-tuning) an AI model on your own reporting style. How can you do that? Let’s break down the steps, the tools, and the considerations involved.
Step 1: Collect Your Past Work
First, you need a dataset – in this case, a collection of your writing that represents the style you want the AI to learn. This could be all your past newsletter issues, articles, blog posts, etc. Ideally, they should be in a format that’s easy to feed into an AI training pipeline (plain text is simplest). Gather them up: perhaps you have them saved, or you might need to scrape your own site or copy-paste from archives.
Quantity matters – the more examples, the better the model can pick up patterns. If you have only 5 articles, the AI might not generalize well. If you have 200, that’s excellent. But even 50k to 100k words of text might be enough to fine-tune a model meaningfully. The content should be fairly representative: if you write both dry news pieces and personal essays, decide which style you want it to emulate, or maybe separate them and train two different style models.
Tip: Clean the data. Remove anything you don’t want the AI to learn. For instance, if all your articles have boilerplate or the same editor’s note at the end, remove those parts. If some pieces are not in your voice (maybe quotes or guest-written sections), you might exclude them. Essentially, give the AI mostly pure examples of your voice. You might even add notes in brackets in some places to inform context, e.g. “[In this article I used a humorous tone]” just if you want it to know context, but that’s optional and advanced.
Step 2: Choose a Model and Fine-Tuning Approach
To train an AI, you don’t start from scratch (that’d be like trying to build your own GPT-4 from nothing – not feasible). Instead, you take a pre-trained language model and fine-tune it on your data. Fine-tuning means adjusting the model’s weights with additional training so it better reproduces patterns in your data.
There are a couple of accessible ways to do this in 2025:
- OpenAI Fine-Tuning: OpenAI allows fine-tuning of some models (like GPT-3.5 Turbo, etc.) on custom data. You’d upload your dataset and train their model on it. For example, you could fine-tune a GPT-3.5 so that it leans towards your style. This is a paid service and you’d need to format your data as prompt-completion pairs (meaning you need to give it a prompt and the desired output style). One way is to use each of your article paragraphs as a “completion” and maybe give a generic prompt like “Write the next paragraph:” for each – the specifics get technical, but OpenAI has documentation on preparing fine-tuning data:contentReference[oaicite:44]{index=44}.
- Open-source Models: There are open-source alternatives like GPT-J, GPT-NeoX, or smaller versions of LLaMA, etc., that you can fine-tune if you have the technical skill. Tools like Hugging Face’s Transformers library provide recipes to fine-tune models on your text. This would require some coding and possibly a decent GPU to run the training. But there are guides – for instance, the novelcrafter link hint suggests a how-to:contentReference[oaicite:45]{index=45}. People have fine-tuned models to mimic Shakespeare, so why not you?
- Prompt-based training (few-shot prompting): This isn’t training the model internally, but an alternative approach: create a prompt that includes examples of your writing and instruct the model to continue in that style. For example: “Here are excerpts from articles by [Your Name] to demonstrate the writing style: [then paste a couple of short representative excerpts]. Now write a new article about [topic] in the same style.” This can work with a powerful model like GPT-4 without formal fine-tuning. It’s less work, but you have to include those examples each time (or use something like OpenAI’s “Custom Instructions” feature to bake some style in). It’s not as precise as actual fine-tuning, but surprisingly effective for some.
If you want the AI primarily for your own use (drafting for yourself), using OpenAI or a local model is fine. If you plan to integrate it into a product or share it, be mindful of license (open-source vs proprietary). Also, fine-tuning might cost money (OpenAI charges based on token count for training data and usage). But it’s not exorbitant if your dataset is smallish. As of now, fine-tuning GPT-3.5 on a few hundred thousand tokens might be in the low hundreds of dollars or less.
Step 3: Train and Evaluate
Once you have your data and method, proceed with training. With OpenAI, you’d upload the files and run their fine-tune command and wait for it to complete. With open-source, you’d run a script and it might take a few hours or more depending on model size and hardware.
After training, you need to evaluate: does it actually mimic your style well? Test it out. Give it a prompt to write something and see if it sounds like you. Perhaps take an article idea you haven’t written yet and ask the model to write it as you would. Compare the result with what you might actually write. It might be a bit eerie to see a machine channeling you! Or it might be off in some ways.
Look for things like: Does it capture your tone (formal, casual, snarky, whatever it is)? Does it structure the piece similarly to how you do (maybe you always start with an anecdote – does the model do that)? Does it use phrases you commonly use (perhaps you often say “However, the fact remains that…” – did it pick such habits up)? It might even mimic your common typos or quirks, which is funny but you can fine-tune further or just fix those manually. Often fine-tuned models get style pretty well but might still need factual guidance – they might write in your voice but still hallucinate facts, because the training was on style, not factuality.
Important: Evaluate not just with one example, but a few. Use different topics to see how it adapts your style to new content. Also, see if it learned any undesirable things – for example, if in your past writing you had some outdated terms or you tended to write long sentences, it might replicate those. Fine-tuning can sometimes exaggerate patterns (like making sentences too long because it thinks that’s your style). You may need to gently correct that by either another round of training with adjustments or by instructing the model during generation (“in [Your Name]’s style but use shorter sentences” could be a prompt trick).
Step 4: Iteration and Fine-Tuning (the other kind)
Fine-tuning an AI isn’t always one-and-done. You might find some issues and iterate:
- If it’s overfitting (just regurgitating exact phrases from your articles rather than writing new text in your style), you might need to reduce training epochs or mix in more varied data.
- If it’s underfitting (not really capturing your voice distinctively), you might need more data or to train longer.
- You could also refine by giving the model some instructions within prompts to correct behavior. E.g., if it’s too verbose, include a guideline in the prompt like “Write concisely in the style of …”. Or if one aspect of your style is missing (say you often use humor but it didn’t show up), you might add some clearly humorous pieces to the training set or just prompt the model to include a joke if appropriate.
Also, consider feedback: maybe show a friend or colleague who knows your writing a sample from you and from the AI and see if they can tell which is which, or what differences they notice. This can highlight what’s not quite there (like “I notice the AI version is less opinionated” or “It uses more cliches than you do”). Then you address those specifically – you could include more of your opinionated pieces in training to push it that way, etc.
Keep in mind, training on your style doesn’t mean it knows facts you know. If you want it also to have your knowledge base, you’d have to include a lot of your reporting content. Even then, it’s not a reliable fact repository. It’s mostly for style/tone/voice. So, likely you’d use this model in conjunction with a larger base model or with your guidance to ensure accuracy.
Using Your Custom Model
Once you’re happy with it, how to use it? If it’s via OpenAI, you’d call the fine-tuned model through their API like you do any model. If local, you’d load it up in a notebook or app and prompt it. Now you have a “mini-you” that can help draft articles, tweets, whatever in your voice.
Practical uses:
- Drafting: Give it bullet points or notes from your reporting, ask it to write a full draft. You get something that sounds somewhat like you. Then you edit it to add the final polish and correct any inaccuracies. This could save time in fleshing out articles.
- Social media: Maybe have it generate 3 tweets summarizing your new article in your style, or a LinkedIn post in your voice. Could be handy to keep a consistent brand voice quickly across platforms.
- Continuity: If you, say, write a weekly column and you fall ill, conceivably your AI-trained model could draft one in a pinch that your editor could lightly edit and publish in your stead – readers might not even know the difference (though ethically you’d likely not want to do that without disclosure; but technically, it’s possible!).
Also, this model might be useful for internal tasks like turning your past text into training data for other things, or generating similar text for practice. For example, if you mentor new writers, you could use the model to generate “here’s how I would write this press release as a story” as a teaching example.
One interesting experiment could be interactive writing: you start a paragraph, have the model continue it, you continue, etc., almost like pair-writing with your AI twin. It might take the story in directions you wouldn’t but that still sound like you, which could surprise you and sometimes pleasantly so.
Caveats and Ethical Considerations
Training an AI on your style does raise some questions:
- Intellectual property: It’s your own work, so presumably fine, but if your work also includes quotes or sections from others, be mindful those ended up in your dataset. The model might accidentally spit out those quotes verbatim in some context, which could be an IP issue. So best to train mostly on your original text.
- Transparency: If you use the model heavily to produce content that gets published under your name, think about transparency or disclosure. In many cases, if you’re still editing it heavily, you might consider it just a tool like any other. But it’s a debate: should journalists disclose AI assistance? Different outlets have different policies. Since this model is basically “you”, you might feel it’s just another way of writing your piece. But still, something to consider in terms of honesty with your audience or editor.
- Over-reliance: As mentioned in other articles, don’t let using this model weaken your own writing skills or creativity. It’s easy to lean on it for a quick draft, but continue to exercise your writing muscles. Use it when it genuinely helps, but not as a crutch for everything.
Also, note that fine-tuning a model means it’s now specialized to your style. It might perform worse on tasks outside that domain. For instance, if you fine-tuned a GPT-3.5 on just your sports articles, and then try to use it to write about quantum physics, it might do a poor job because it’s biased to sports style and content. For general tasks, you might still use the base model. Your fine-tuned model is like a mode you enter for tasks where your voice matters most (like writing your columns or newsletters).
Conclusion: Your Own “AI Twin” Writer
Training an AI model on your reporting style can be a fascinating and useful journey. It’s akin to creating a digital doppelgänger that knows your writing soul. While it takes some effort to set up, the payoff could be writing assistance that feels far more personal than generic tools. It’s an advanced move in the world of AI-assisted journalism, but one that more writers are likely to try as tools become more user-friendly.
By following the steps – gathering your work, fine-tuning a model, and iterating – you’ll learn a lot about what defines your style in the process. You might see patterns in your writing you never noticed until you saw the AI emulate you. That self-awareness alone can make you a better writer. And with your custom model, you have a new kind of collaborator who’s always available and knows your voice intimately.
No AI will ever capture 100% of the spark that makes your writing yours – that ineffable human touch. But even a 70% good mimic can handle grunt work and let you focus on adding the brilliance on top. It’s still you doing the journalism, the AI just helps with the copy.
As one expert nicely put it, “AI should assist, not replace” – and when the AI is trained on you, it’s truly assisting you in a tailored way.
If you’re tech-savvy or adventurous, give it a shot. Even a partial success can feel like magic. And as always, if you need more tips on the technical how-tos or want to read about other journalists’ experiences doing this, make sure to subscribe to our newsletter (written in a human-meets-AI style!). We share practical guides and experiments – like journalists fine-tuning their own GPT clones or open-source models – so you can learn from pioneers in this space. Who knows, maybe one day your AI twin and mine can have a chat while we sip coffee and focus on the bigger picture!