What is RAG?
LLM Engineering
RAG (Retrieval-Augmented Generation) — A technique that enhances LLM responses by retrieving relevant documents from an external knowledge base and including them as context in the prompt. This grounds the model in factual, up-to-date information.
FAQ
What does RAG stand for?
Retrieval-Augmented Generation. It combines information retrieval with text generation to produce more accurate, grounded responses.
When should I use RAG?
When you need an LLM to answer questions about your own documents, when knowledge changes frequently, or when you need source citations.
RAG vs fine-tuning?
RAG retrieves knowledge at query time (always up-to-date, citeable). Fine-tuning bakes knowledge into model weights (faster inference, better style control). Many production systems use both.
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