Retrieval-Augmented Generation Track

The TREC Retrieval-Augmented Generation Track fosters innovation and research in retrieval-augmented generation systems: combining retrieval methods that find relevant information within large corpora with Large Language Models, to help systems produce relevant, accurate, updated, and contextually appropriate content.

The track brings the research community together around a unified benchmark to evaluate the end-to-end performance of systems that combine retrieval and generation. Distinct but complementary tasks enable deeper analysis of individual components and their interactions.