Why Does RAG Still Hallucinate?
Retrieval-Augmented Generation is often presented as the solution to hallucination, ground the model in external knowledge. Yet RAG systems produce their own distinctive hallucinations. What’s happening?
Retrieval failures: The query may not surface relevant documents. Ambiguous queries retrieve tangentially related content. The knowledge base may simply lack the needed information.
Context awareness failures: Even when correct information is retrieved, models may ignore it. Attention mechanisms don’t guarantee the model will use what it has access to, particularly with long contexts.
Context alignment failures: The model may selectively attend to retrieved content, incorporating some pieces while contradicting others. Or it may “blend” retrieved facts with parametric knowledge, producing chimeric outputs.
Noise amplification: Irrelevant or incorrect retrieved content can introduce new hallucinations that wouldn’t have occurred with the base model alone.
The implication: RAG shifts the hallucination problem, it doesn’t eliminate it. Different failure modes require different detection and mitigation strategies than pure LLM hallucinations.