What is Retrieval-Augmented Generation (RAG) and how does a basic RAG pipeline work?

For AI / LLM Engineer ML Engineer Data Scientist

The short answer

RAG augments an LLM by retrieving relevant documents from an external knowledge store at query time and feeding them into the prompt as grounding context. A basic pipeline chunks and embeds documents into a vector store, retrieves the top-k most similar chunks for a query, and the LLM generates an answer conditioned on them, reducing hallucination and keeping knowledge current.

How to think about it

RAG augments an LLM by retrieving relevant documents from an external knowledge store at query time and feeding them into the prompt as grounding context. A basic pipeline chunks and embeds documents into a vector store, retrieves the top-k most similar chunks for a query, and the LLM generates an answer conditioned on them, reducing hallucination and keeping knowledge current.

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Keep practising

What is Retrieval-Augmented Generation (RAG) and why is it used? Your RAG system is hallucinating even though the correct context was retrieved. How do you debug it? How do you evaluate the quality of an LLM or RAG system? What chunking strategies exist for RAG and how do you choose between them? In LlamaIndex, what are nodes and query engines, and how is RAG exposed as a tool to an agent?

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