How to Fine-Tune AI to Write in Your House Style
For Content marketing teams · Based on Zen van Riel Local AI Fine-Tuning Pipeline
// TL;DR
Content marketing teams can use this pipeline to fine-tune an open-source model on existing on-brand blog posts so it produces house-style content — specific heading structure, sentence length, and vocabulary — without injecting a multi-paragraph style guide into every prompt. You collect on-brand posts, engineer prompt-response pairs using a consistent template, train a LoRA adapter, and evaluate whether the model adheres to your style with minimal prompting. Use it when even detailed style-guide prompts fail to produce consistent formatting.
Why does our AI ignore our style guide?
Because prompt engineering can't escape the base model's trained defaults — that's the prompt ceiling. You paste a detailed style guide into every prompt, and the model still drifts to its own heading patterns and sentence rhythm by paragraph three. Prompts describe your style; they don't embed it. Fine-tuning bakes your house style into the model's weights so adherence becomes the default behaviour, not something you have to re-argue in every prompt.
When should a content team fine-tune?
Run the decision ladder first. If a sharper prompt produces on-brand output, stop — you've saved a weekend. Add RAG if the model needs to reference brand assets. Try an agentic loop if orchestration helps. Only when all three fail to produce consistent house style is fine-tuning warranted. For formatting and voice specifically, fine-tuning tends to win, because consistent structure is an embedded behaviour that prompts and retrieval struggle to enforce reliably at scale.
How do we build the training dataset?
Your corpus is your library of on-brand blog posts. Dataset engineering is the make-or-break step. Clean everything first — the model bakes in whatever noise it sees, so inconsistent formatting in your training data means inconsistent output.
Then build chat-format prompt-response pairs with a deliberately consistent template. Every prompt should look like 'Write a blog post on this topic: {topic}' and every response should be a correctly formatted example post. This is the trick that embeds your formatting rules: because the prompt structure is identical across all pairs, the model learns that this input format always maps to your house-style output. The formatting becomes intrinsic rather than prompted.
How do we prove it worked?
Evaluate with minimal prompting. Request posts using only the bare template — no style guide attached — and check for unprompted adherence to house style: heading structure, sentence length, vocabulary. Run the same minimal prompts on the vanilla base model to see the contrast. If the fine-tuned model misses, the fix lives in your dataset (inconsistent templates or off-brand examples) or LoRA parameters — never in bolting a style guide back onto the prompt, which defeats the purpose.
On hardware: ensure the model fits in dedicated VRAM, use Nvidia with CUDA, validate parameters on a short run, and budget 2–3 hours per full training run. Disable behaviours you don't want, like extended chain-of-thought, during training so drafts come out clean.
Export the merged model to GGUF and run it in LM Studio or Ollama, giving your whole team a shared on-brand drafting engine.
What's our first move?
Pull together your strongest on-brand posts and audit them for formatting consistency — inconsistency here becomes inconsistency in output. Draft your standard prompt template now, since every training pair must use it. Then run the decision ladder honestly before committing to the pipeline, so you only fine-tune if prompts and RAG genuinely can't hold your house style.
// FREQUENTLY ASKED QUESTIONS
Why use a consistent prompt template in training pairs?
Because identical prompt structure across all training pairs teaches the model that this input format always maps to your house-style output. When you use a template like 'Write a blog post on this topic: {topic}' everywhere, the formatting rules become embedded in the model — so at inference, minimal prompting still produces correctly formatted, on-brand posts.
How many blog posts do we need to fine-tune house style?
Quality and consistency matter more than raw count. Aim toward the pipeline's 1–2 million+ token guideline for a mid-sized model, but prioritize posts that are cleanly and consistently formatted. A smaller set of tightly on-brand examples beats a large set with mixed formatting, since the model bakes in whatever patterns it sees.
Can the whole team use the fine-tuned model?
Yes. After exporting to GGUF, run it in LM Studio or Ollama and your entire content team can draft from the same on-brand model locally. Everyone gets consistent house-style output with minimal prompting, without each writer maintaining their own giant style-guide prompt.