How to Fine-Tune an LLM on Your Own Data

Fine-tune an LLM to teach it a consistent style or format — but reach for retrieval first when you need it to know facts

Fine-tuning takes an existing open model such as Llama, Mistral, or Qwen and continues its training on your own examples, adjusting its weights so it reliably produces the tone, format, or behaviour you want. It is genuinely powerful for shaping how a model responds. But it is the wrong tool for teaching a model what it knows — for that, retrieval-augmented generation, which feeds the model your documents at query time, is cheaper, faster to update, and usually better. Knowing which problem you actually have is the most important decision in the whole process, and getting it wrong wastes weeks.

Fine-tuning versus retrieval: pick the right tool

These two approaches solve different problems, and the most common mistake is fine-tuning when you should have used retrieval.

• Use retrieval (RAG) when the model needs to answer from your facts — your policies, your product details, your latest data. You update the knowledge by changing documents, not by retraining, so it stays current effortlessly.
• Use fine-tuning when you need the model to consistently adopt a style, voice, or output format, or to handle a specialised task that prompting alone cannot pin down reliably.
• Use both in advanced cases — a fine-tuned model for behaviour, retrieval for knowledge — but only once you have proven you need each.

What fine-tuning actually requires

Fine-tuning needs a dataset of examples in the form of inputs paired with the outputs you consider ideal — the prompts your model will see and the responses you wish it would give. Quality matters far more than quantity here: a few hundred clean, consistent, representative examples beat thousands of noisy ones, because the model learns exactly what you show it, flaws included. Assembling and curating that dataset is the real work of fine-tuning, and it is where most of the effort and most of the eventual quality come from. The training run itself is almost an afterthought by comparison.

Most teams who think they need fine-tuning actually need better prompting or retrieval. Exhaust those cheaper, faster options first. Fine-tuning is the right answer surprisingly rarely, and it is the expensive answer always.

Efficient fine-tuning is now accessible

The good news is that fine-tuning no longer demands a data centre. Parameter-efficient methods, most notably LoRA and its quantized variant QLoRA, train only a small set of additional weights rather than the entire model, which slashes the hardware and time required. With these techniques you can fine-tune a mid-sized open model on a single capable GPU, and sometimes on rented cloud GPU time for a modest cost. This is what has moved fine-tuning from a frontier-lab capability to something a well-organised team can genuinely undertake, provided the dataset is there.

Evaluate honestly, then decide

A fine-tuned model is only better if you can prove it. Before you start, build a held-out test set of examples the model never trained on, and define what "better" means for your task in measurable terms. After fine-tuning, compare the new model against the base model on that set, and be willing to conclude that the gain did not justify the effort — that result is common and is itself valuable information. Fine-tuning also locks in a snapshot: when your needs shift, you retrain, whereas a retrieval system updates by editing documents. Weigh that ongoing maintenance honestly before you choose the path that ties your behaviour to a training run.

Watch for overfitting and forgetting

Two failure modes catch first-time fine-tuners, and both are avoidable once you know to look for them. Overfitting happens when you train too long or on too few examples, and the model memorises your training data instead of learning the general pattern — it dazzles on examples it has seen and stumbles on anything new. The second is catastrophic forgetting, where aggressive fine-tuning erodes the broad abilities the base model arrived with, leaving it sharper on your narrow task but worse at everything around it. Your held-out test set catches the first, and including some general examples alongside your task-specific ones guards against the second. Both are reasons to fine-tune gently and measure constantly rather than training as hard as the hardware allows. The instinct to squeeze every last drop from a training run is exactly what produces a brittle model, so stop at the point where your test scores plateau rather than pushing until they start to slip.

Prefer it built and managed for you?

The hardest part of fine-tuning is not the training — it is deciding whether you need it at all, then building the dataset and the evaluation that make it worthwhile. BSH Technologies helps you choose correctly between prompting, retrieval, and fine-tuning, and executes whichever path the evidence supports. Before you invest in retraining, talk to BSH Technologies or browse our AI & automation services.