RAG
technique · Techniques
Retrieval-Augmented Generation answers a question by first retrieving relevant passages from a knowledge source and then having a language model compose the answer from them. It solves two problems at once: a model cannot know facts outside its training data, and fine-tuning it on new facts is expensive and must be repeated whenever the facts change. It exists because most factual answers are cheaper to look up than to memorise. The cost is a new dependency chain - embeddings, a vector store, reranking and a generation step - which is why it is the most interconnected node in this graph.