When teams start building with large language models, one of the first architectural decisions is whether to use retrieval-augmented generation (RAG), fine-tuning, or both.
When RAG is the right choice
RAG connects a model to your documents and databases at query time. It is ideal when information changes often, when answers must cite sources, and when you want to avoid training on sensitive data.
When fine-tuning helps
Fine-tuning adjusts a model’s behavior: tone, format, or specialized classification tasks. It works best with stable, well-labeled examples.
Our recommendation
Most enterprise assistants should start with RAG plus strong evaluation. Add fine-tuning later for narrow tasks where the numbers justify it. Either way, invest early in an evaluation suite so every change is measured.