Practical checklists and governance patterns to manage AI privacy, model risk, explainability, and human oversight.
Practical guardrails, retrieval and prompt patterns, provenance practices, human-in-the-loop handoffs, evaluation metrics, privacy controls, and a brief rollout runbook for building retrieval-augmented assistants that support organizational knowledge work.
A practical, actionable guide for designing retrieval‑augmented, role‑aware assistants that surface institutional knowledge safely. Includes architecture patterns, prompt templates, provenance and explainability approaches, guardrails to reduce hallucination and data leakage, update/retirement workflows, monitoring metrics, escalation rules, and two concrete example flows (employee onboarding Q&A and incident triage).