@h100envy: This paper completely changed how I think about trusting retrieval in RAG: Fetch documents -> Score their quality -> Ge…

This paper completely changed how I think about trusting retrieval in RAG: Fetch documents -> Score their quality -> Get a confidence -> Pick an action -> Clean the context -> Generate Here is the 5-step blueprint: Retrieval evaluator: a lightweight model scores the quality of the fetched docs for the query and outputs a confidence degree. Three actions: confidence triggers one of {Correct, Incorrect, Ambiguous}, instead of blindly stuffing everything in. Web search on failure: if docs are bad, the query is rewritten and knowledge is pulled from large-scale web search rather than a static corpus. Decompose-then-recompose: each document is split into minimal strips, relevant ones are kept, noise is dropped, the context is rebuilt. Plug-and-play: all of this bolts onto plain RAG and onto Self-RAG with no retraining of the generator. Key insight: the RAG problem is not only when to retrieve, but what to do when retrieval comes back wrong. One lightweight evaluator with three actions lifts both plain RAG and SOTA Self-RAG across four datasets at once. Read this, then check the article below.