Explainer
RAG or fine-tuning? Both.
The two are not rivals — they answer different questions. Here is the plain-English difference, when each wins, and why we usually combine them.
RAG (retrieval)
Look it up, then answer
- The model searches your documents and answers with the exact passage, source cited.
- Best for facts that change often: prices, contracts, procedures, catalogue.
- Update instantly — just change the document, no retraining.
- Strong against invented answers (provenance).
Fine-tuning (adapters)
Teach it your domain
- Adapters trained on your data so the model absorbs your vocabulary, style and rules.
- Best for how you work: tone, format, reasoning specific to your trade.
- Makes the model fluent in your field, not just able to look things up.
- Lightweight adapters — fast, cheap, updateable, no full retrain.
Why we combine them
Alone, RAG can retrieve the right fact but phrase it like a generalist; fine-tuning can sound like you but drift on fast-changing facts. Together, the model reasons in your domain AND grounds every answer in your live data — with the source traceable. That is what makes a private model both trustworthy and genuinely yours.
Do I have to choose one?
No. For most enterprise cases we combine both: RAG for exact, up-to-date facts, adapter fine-tuning for your domain’s language and reasoning.
Which is cheaper?
RAG needs no training, so it is the fastest start. Adapter fine-tuning is lightweight and far cheaper than retraining a full model — and both run locally at a fixed cost.
Which avoids hallucinations?
RAG is the strongest lever: answers are grounded in your real documents with the source cited. Human validation stays in the loop on what matters.
Talk to us about your data