How RAG Reduces Hallucination

RAG reduces hallucination by conditioning generation on retrieved evidence rather than relying solely on parametric memory.

When a pure LLM generates text, it draws on patterns learned during training. If the needed knowledge wasn’t in training data, or if the model hasn’t reliably encoded it, the model may fabricate plausible-sounding but incorrect content, hallucination.

RAG changes the generation dynamic: the model is explicitly provided with retrieved documents as context. The generator learns to copy, rephrase, or synthesize information from these documents. When the retrieved evidence is accurate and relevant, the generation is grounded in that evidence.

The mechanism isn’t foolproof. RAG can still hallucinate if:

• Retrieved documents don’t contain needed information
• Retrieval returns irrelevant or incorrect documents
• The generator ignores or misinterprets the retrieved content
• The generator extrapolates beyond what documents support

But empirically, RAG systems produce more specific and factually correct outputs than parametric-only generators on knowledge-intensive tasks.

Related: 05-atom—rag-definition