Guide

What is RAG?

Retrieval-Augmented Generation for grounded AI responses.

RAG combines information retrieval with LLM generation. Instead of relying solely on training data, RAG retrieves relevant documents and includes them in the prompt context.

How RAG Works

  1. 1. User query is embedded into a vector
  2. 2. Similar documents retrieved from vector store
  3. 3. Retrieved docs added to LLM prompt
  4. 4. LLM generates response using context

RAG Benefits

  • Reduces hallucinations with grounding
  • Keeps knowledge current without retraining
  • Enables source attribution
  • Works with any LLM

Does RAG eliminate hallucinations?

No. RAG reduces but doesn't eliminate hallucinations. Models can still generate content not in retrieved docs. Monitor with hallucination detection.

Monitor RAG pipelines

Start Free