GenAI SDLC: Governance, Compliance & Pro Engineering Metrics

About this course

This course contains the use of artificial intelligence.

Ship GenAI products with evidence, not guesswork.

GenAI projects do not fail only because the model performs poorly. They fail when teams cannot define ownership, evaluate real risks, document decisions, or prove that a release is ready.

In this hands-on course, you will build a practical governance and measurement system for GenAI applications across the software development lifecycle. You will learn how to apply governance to chatbots, RAG applications, copilots, and agentic workflows without creating a slow, bureaucratic approval process.

Who this course is forAI product managers launching GenAI features

Engineering managers and software leaders responsible for delivery qualityAI, ML, MLOps, and platform engineers building GenAI applications

Security, privacy, risk, and compliance professionals supporting AI programs

Consultants and transformation leaders designing responsible AI operating models

What you will learn

Define GenAI system boundaries across models, data, prompts, retrieval, tools, and people

Classify use cases with a practical risk-tiering model

Create a governance RACI with clear decision rights and escalation paths

Build a traceability record for model, prompt, data, test, and release changes

Design evaluation datasets that reflect real user tasks and failure modes

Measure quality, groundedness, safety, privacy, security, cost, latency, and user reliance

Set release gates with thresholds, owners, evidence, and residual-risk decisions

Test for prompt injection, data leakage, unsupported answers, and unsafe tool actions

Monitor production behavior and define rollback and incident-response triggers

Gover

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