This episode introduces “Speculative RAG,” a Google research framework designed to improve the speed and accuracy of Retrieval Augmented Generation (RAG) by moving away from traditional “brute-force” methods that overwhelm Large Language Models. This new approach operates via a “Draft-then-Verify” pipeline where a smaller RAG Drafter generates multiple answer drafts and accompanying rationales (logical explanations) from clustered, diverse subsets of documents. A larger RAG Verifier then efficiently evaluates these drafts based on the quality of the rationale, applying a combined confidence score influenced by self-consistency and self-reflection. The framework's implications suggest that logical consistency and content diversity will become crucial factors for authority in future AI-driven search environments, requiring content creators to explicitly bridge evidence and conclusions to assist in rationale generation.

https://www.kopp-online-marketing.com/patents-papers/specualtive-rag-enhancing-retrieval-augmented-generation-through-drafting