AI Campus • Practical resources
AI Product Strategy & Safety
Define a user outcome, operational boundaries and a realistic validation plan for an AI feature.
Before you start
- One concrete user problem and access to feedback from a potential user; no paid tools needed.
The reading and planning exercises need no paid course or API. Optional inference, training, IDE features and hosted services may require hardware, accounts or paid access. Use the local or manual alternative before spending money.
Start with the official learning material
Free-to-read references checked 3 November 2026. Reading documentation does not activate a provider or create a subscription.
Practice with a concrete exercise
1. Write a one-page feature brief
Read the framework overview. Name the user, current workflow, success measure and one condition where AI should not act. Separate product assumptions from evidence you already have.
2. Map failure and recovery
List five plausible failures such as unsupported answers, data exposure or mistaken delivery. For each, name a prevention measure, a detection signal, a human owner and a recovery step. Avoid presenting a guardrail as a guarantee.
3. Test a small value proposition
Show a clearly labeled prototype or paper workflow to a potential user. Record what they actually tried, confusing points and whether the task was completed. Estimate cost using explicit volume and price assumptions rather than made-up revenue.
Check what you learned
- The success measure describes user behavior rather than only model output.
- Failure cases have observable signals and an owner.
- Research findings, assumptions and untested plans are labeled separately.
Keep your artifact, observed checks and remaining limitations in a short README. Mark any exercise you only designed rather than executed.