Corrective RAG and Adaptive Retrieval in Production: Architecture, Confidence Scoring, and Web Fallback Loops
Standard retrieval-augmented generation (RAG) pipelines follow a rigid, feed-forward paradigm: embed the query, fetch top-$k$ chunks via vector or hybrid search, inject the raw chunks into the prompt context, and sample a completion. While functional for homogeneous corpora with high semantic overlap, this naive retrieve-and-generate approach degrades rapidly in production environments. When a vector index returns irrelevant, noisy, or out-of-domain chunks, the generator inevitably suffers from
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