Complete procedures with a way back.

Build a bounded research service that combines current web evidence with a private vector store, renders clickable citations, exposes every consulted source, limits retrieval, records cost, and refuses unsupported conclusions.

Build a governed RAG knowledge lifecycle for document updates and deletion, immutable lineage, tenant ACLs, PII minimization before embeddings, retention and legal hold, multi-index consistency, deletion verification, encrypted backup, isolated restore, derived-index rebuild, and rollback that never resurrects prohibited data.

Build a production RAG evaluation program that separates retrieval recall and precision from answer faithfulness, citation validity and support, correctness, usefulness, and abstention; calibrates automated graders with humans; blocks security regressions; and carries evidence through regression, canary, continuous evaluation, and rollback.

Design and release a production RAG retrieval pipeline that preserves exact-term BM25 strength, adds semantic vector recall, fuses rankings with reciprocal rank fusion, reranks a bounded set, applies tenant ACLs before every branch, and proves quality, latency, cost, citation, and rollback behavior.

Govern source revisions, preserve ACLs through parsing and chunking, pin embeddings, combine lexical and semantic retrieval, validate claim-level citations, evaluate abstention, and release immutable indexes with safe rollback.

Build an evidence-governed answer pipeline with authoritative sources, ACL-safe retrieval, claim-level citations, independent verification, empirical abstention thresholds, representative evals, and coupled rollback.