Retrieval-Augmented Generation promises grounded, factual AI — but it often creates expensive search engines instead of reasoning systems. This episode digs into the actual mechanics: similarity score cutoffs, dynamic top-k tuning, model-gated retrieval, and prompt framing that preserves generative agency. Then we tackle the harder problem — architecting systems with multiple retrieval sources (episode archives, memory layers, live web) and deciding whether to route, fuse, or let the model choose. We work through Reciprocal Rank Fusion, source weighting, freshness signals, and when agentic tool selection beats pre-built pipelines. This is how the show itself works, diagnosed in real time.
Episode #177067 — open it directly at myweirdprompts.com/177067