As soon as it comes to connecting a language model to knowledge specific to a company, the same question comes up: should you use RAG (retrieval-augmented generation) or fine-tuning? The two approaches address different needs.
RAG: giving access to up-to-date documents
RAG works by retrieving the relevant information from a document base at the moment of the question, then feeding it to the model as context. It's the approach to favor when your data changes often and when you need traceability: you can know exactly which document was used to generate an answer.
- Near-instant knowledge updates
- Citable sources, useful for trust and auditing
- Generally lower setup cost
Fine-tuning: learning a style or a behavior
Fine-tuning involves partially retraining a model on specific examples. It's relevant when the challenge isn't access to information, but adopting a tone, a response format, or a particular behavior that stays stable over time.
RAG answers "what information should the model know?". Fine-tuning answers "how should the model behave?".
What if we combined both?
In practice, many robust systems combine the two: light fine-tuning to set the tone and format, and RAG for access to up-to-date data. The priority, however, remains to start simple: RAG alone is enough for most enterprise use cases.
Our recommendation
Unless you have a very specific behavioral need, we almost always recommend starting with RAG: faster to set up, easier to evolve, and more transparent for your teams.
