What is RAG, and when should you choose it over fine-tuning?

One of the most persistent misconceptions among enterprise engineering leads is that fine-tuning an open-source model is the best way to teach it company knowledge.

Fine-tuning alters the internal weights of a neural network. It is ideal for teaching a model specific syntactical styles, tone of voice, or rigid output schemas (such as SQL generation or JSON compliance). However, using fine-tuning as a fact repository is fraught with peril: models hallucinate when memorizing facts, updating the knowledge base requires costly retraining runs, and cite-tracing back to a source sentence is mathematically impossible.

Retrieval-Augmented Generation (RAG) decouples knowledge storage from reasoning. Your proprietary documents remain in a searchable vector and keyword database. At inference time, the system retrieves only the relevant paragraphs and feeds them into the model's context window with explicit instructions: 'Answer only using the provided sources.'

For 95% of corporate knowledge applications, a well-tuned hybrid RAG pipeline with cross-encoder rerankers will outperform fine-tuning at a fraction of the maintenance burden.