Complete procedures with a way back.

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.

Create an owned prompt registry that safely references OpenAI reusable prompts from the Responses API, validates typed variables, pins platform versions, proves consumer compatibility, evaluates candidates, and supports controlled promotion and complete rollback.

Engineer a production prompt as a versioned, evidence-grounded application contract with explicit instruction hierarchy, safe abstention, representative evals, deterministic gates, controlled rollout, observability, and atomic rollback.

Create a privacy-governed, versioned evaluation corpus and regression system with deterministic and calibrated model graders, baselines, slices, complete coverage, failure analysis, CI gates, deprecation-aware versioning, shadow rollout, monitoring, and complete release rollback.

Build minimal, privacy-reviewed few-shot demonstrations for classification, extraction, and constrained generation; prove their value against zero-shot baselines, sealed holdouts, slices, ablations, permutations, counterfactuals, adversarial tests, and reversible rollout.

Measure production token economics and user latency, design cacheable prompts and context budgets, and ship only quality-gated optimizations.

Design, validate, evaluate and operate schema-constrained OpenAI responses without confusing valid JSON with factual truth or authorization.