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 long-document assistant that validates and quarantines PDFs, chooses inline or Files API intake deliberately, enables native citations, preserves page-aware citation blocks, refuses unsupported conclusions, and lets reviewers inspect every claim against the original page.

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.

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.

Build an evidence-first support bot that crawls one authorized website through sitemap and robots controls, blocks SSRF, normalizes canonical pages, creates an OpenAI File Search index, returns inspectable citations, evaluates every revision, exposes an explicit human-escalation path, and refreshes content without replacing a known-good index.