Techniques & Methods
RAG vs Fine-Tuning
When to use RAG (retrieval) versus fine-tuning (continued training) — the central architectural choice.
RAG vs fine-tuning is one of the most common architectural choices in building AI products. RAG (Retrieval Augmented Generation) leaves the model unchanged and provides relevant information at query time by searching a document store. Fine-tuning permanently adjusts the model on your data. Quick rules: use RAG when your knowledge changes frequently, you need citations, or you have a lot of documents. Use fine-tuning when you need a specific output style, tone, or format that prompting cannot reliably enforce. Many production systems use both. RAG is usually the cheaper, simpler starting point.