When a company decides AI should "know" its business, the first question is usually: should we train our own model? In most cases the answer is no. Here is why.
What fine-tuning is
Fine-tuning is additional training of an existing model on your examples. The model changes its behavior: it writes in a certain style, follows a format and gets better at a specific task (for example ticket classification).
What RAG is
RAG does not change the model. Before each answer it searches your documents and gives the model relevant passages as context. The model answers based on them and cites the source. We explained the basics in what is RAG.
Comparison
| Criterion | RAG | Fine-tuning |
|---|---|---|
| Up-to-date information | Immediately, as soon as a document is added | Only after retraining |
| Source citations | Yes | No |
| Risk of hallucination | Low | Higher |
| Per-user permissions | Yes | No |
| Implementation cost | Medium | Higher (data, training, evaluation) |
| Best for | Knowledge, documents, regulations | Style, format, narrow tasks |
When to choose RAG
- knowledge changes often (prices, regulations, procedures),
- users must see the source,
- different users may see different documents,
- you want results within weeks.
When fine-tuning makes sense
- the model must follow a very specific format or professional jargon,
- you have thousands of high-quality input and output examples,
- the task is narrow and repetitive (classification, extraction),
- you want a smaller, cheaper model that runs locally.
Combining both
For demanding projects we combine them: a smaller model adapted to terminology and format, with RAG for facts. You get both accuracy and a lower cost per request.
Our recommendation
Start with RAG. For 90% of companies it solves the problem faster, cheaper and more safely. Leave fine-tuning for later, when real usage data shows exactly where it is needed.
See our AI document assistant service or send an inquiry.
Frequently asked questions
Does fine-tuning add knowledge to a model?
Partly and unreliably. Fine-tuning is great for style, format and specific tasks, but for changing facts RAG is more accurate and cheaper.
Can I combine RAG and fine-tuning?
Yes. Fine-tuning can teach a model your format and terminology, while RAG provides up-to-date facts with sources.
Tell us what slows you down
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