Dagstuhl-Seminar — Retrieval-Augmented Generation – The Future of Search?

Retrieval-augmented generation (RAG) has proven effective in conditioning the output of large language models (LLMs) on relevant documents and for grounding LLM-generated statements, this way combatting the so-called confabulation or hallucination problem. Basically, RAG combines (1) a retrieval phase, where a search system identifies relevant documents for a user prompt, and (2) a generation phase, where an LLM synthesizes a tailored answer, probably linking to the retrieved sources.