AI Product Management for Production LLM Lifecycles
summary
Argues that production LLMs need concrete product requirements: require explainable responses with cited sources, access controls and end-to-end audit logs for high‑risk flows like a financial advisory assistant, and measurable gates for fairness, safety, and robustness. Recommends scenario-based testing (ambiguous, adversarial, edge cases), continuous monitoring of technical and trust metrics, and governance artifacts such as model cards plus vendor checks against OECD and NIST frameworks. Emphasizes human‑in‑loop verification, override paths, and continuous retraining and auditing rather than one‑time validation.
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chapters (11)
- 0:00Responsible AI Overview
- 0:42Why LLMs Break Lifecycle
- 2:32PM Role and Shift Left
- 3:38Lifecycle Practices and Controls
- 4:50Trust Requirements by Design
- 7:56Governance Artifacts and Data Semantics
- 9:13Scenario Testing and Failure Modes
- 12:53Monitoring Trust and Metrics
- 15:55Frameworks OECD and NIST
- 17:48Human Centered Oversight
- 19:46Closing Trust First Mindset
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