RAG reduces hallucinations and outdated knowledge by retrieving current, verifiable information from a specified knowledge base and feeding it into the generative model. This grounds the LLM's response in retrieved evidence rather than relying only on pre-trained data, which may be stale or incomplete.

RAG systems combine a retriever with a generative LLM. The retriever searches a knowledge base, such as a research database or document set, for content relevant to the query. The generative model then uses that retrieved content to craft an answer, which is more accurate, relevant, and factually grounded. Because the text is generated from retrieved information, the system can supply up-to-date and contextually appropriate answers based on verifiable data, directly addressing hallucinations and the problem of models trained only on data available up to a cutoff date.

Key points

RAG has a retriever module that finds relevant items from a knowledge base.
The generative module uses those retrieved items to produce factually grounded responses.
Generating text from retrieved knowledge reduces hallucinations compared to relying on memorized training data.
Retrieval from a current knowledge base overcomes the limitation of outdated training data.
The technique enables up-to-date, verifiable, contextually appropriate answers.

How does RAG reduce LLM limitations such as hallucinations and outdated knowledge?

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