Two approaches, one goal
RAG (Retrieval-Augmented Generation) and fine-tuning are the two primary strategies for making LLMs work with your specific data. The right choice depends on your data, your latency requirements, and your budget.
RAG retrieves relevant context at query time and feeds it alongside the user's question. Fine-tuning adjusts the model's weights using your training data, baking knowledge directly into the model.
When to use RAG
Use RAG when your data changes frequently, when you need source attribution, or when you can't afford the compute costs of fine-tuning. RAG is also the safer starting point — it's easier to debug and iterate on.