RAG or fine-tuning? How to choose for your business data
They solve different problems. Picking the wrong one is the most expensive mistake we see in generative AI projects.
"We want to train a model on our data" can mean two very different things. Sometimes the model needs to know facts from your documents. Sometimes it needs to behave differently: follow your format, use your terms, sound like your brand. The first is a retrieval problem. The second is a fine-tuning problem.
Retrieval-augmented generation (RAG)
RAG leaves the model unchanged. When a question comes in, the system searches your documents, pulls the most relevant passages and gives them to the model to answer from.
- Good for: policies, product catalogues, contracts, knowledge bases, anything that changes often.
- Strengths: answers can cite sources, updates are instant, permissions can follow the user.
- Weaknesses: only as good as the search. Poorly structured documents give poor answers.
Fine-tuning
Fine-tuning adjusts the model itself using examples of the input and output you want.
- Good for: consistent formats, classification, tone of voice, domain jargon, Arabic dialect handling.
- Strengths: shorter prompts, faster and cheaper inference, more consistent behaviour.
- Weaknesses: facts go stale, retraining is needed for updates, and it needs good examples.
The usual answer: both, in order
Start with a good model and retrieval. Measure against an agreed test set. If the answers are right but the format or tone is off, fine-tune. If the answers are wrong because the facts are missing, fix the retrieval. Training is rarely the first fix.
Write the test before you choose the technique. It settles most arguments in an afternoon.
Our generative AI modeling engagements always start with that test set.
