Practical patterns, guardrails, and playbooks to safely apply AI and automation for capturing knowledge, improving search, and scaling routine decisions.
A practical, interactive checklist for designing safe, explainable retrieval-augmented assistants. Guides teams through scoping, data sources, retrieval strategy, prompt templates, human-in-the-loop controls, validation tests (including simple hallucination detection), logging, access controls, and monitoring.
An actionable, savable checklist teams can use to assess readiness before introducing AI-assisted tools into workflows. Includes fields to capture approvals, effort estimates, risk scoring, ownership, and notes to support prioritization and follow-up.
A practical, reusable matrix-style checklist teams can use to evaluate privacy, bias, explainability, data minimization, security, human oversight, vendor risk, and operational safety before advancing an assistant or automation from pilot to production. Includes concrete evidence examples, rating guidance, sample mitigations, and suggested production thresholds.