RAG

Retrieval-Augmented Generation (RAG)

AI, ML
AI, Generative AI, LLM, RAG

Retrieval-Augmented Generation (RAG) #

Retrieval-Augmented Generation (RAG) is a system design pattern that improves an LLM’s answers by:

  1. Retrieving relevant information from an external knowledge source, and then
  2. Augmenting the LLM prompt with that retrieved context before generating the final response.

RAG helps an LLM look things up first, then answer using evidence.

Why RAG is Useful #

RAG is commonly used when:

  • Your knowledge is in private documents (PDFs, policies, internal wiki)
  • You need up-to-date information (things not in the model’s training data)
  • You want fewer hallucinations by grounding answers in retrieved sources
  • You want traceability (show “where the answer came from”)

RAG does not change the model weights.
It changes what the model sees at inference time by adding retrieved context.