Course Objective & Scope
In traditional software discovery, showing clickable Figma mocks and asking 'Would you use this?' often suffices; in AI products, the biggest risk is that the model fails to hit acceptable quality once exposed to messy real-world data. This course teaches AI product discovery methods that test user desirability and model feasibility together from day one.
Participants run rapid prompt and prototype spikes on real sample data for their own product ideas, define Minimum Viable Quality (MVQ) thresholds, and produce an engineering-ready AI feature brief and evaluation plan.
Who Should Attend?
- Product managers (PMs), product owners (POs) and business analysts
- UX researchers, product designers and innovation teams
- Lead engineers participating in product discovery
- No coding prerequisites
Key Takeaways
- Separate user problems that genuinely need an LLM or agent from those better solved with rules or cleaner UX
- Run live data prototypes and Wizard-of-Oz experiments instead of relying on static mockups
- Define the Minimum Viable Quality (MVQ) bar based on error cost and user effort to verify
- Curate an initial 30–50 example golden evaluation set during discovery
- Write an AI feature brief covering scope, edge cases, fallback behaviour and acceptance criteria
Curriculum
From Deterministic to Probabilistic Product Discovery
- How discovery changes when system outputs are non-deterministic
- Problem–capability fit: knowing when NOT to use AI
The Four Risks: Value, Usability, Feasibility and Viability/Trust
- Cost-of-error versus effort-to-verify matrix
- Validating data availability, privacy constraints and latency early
Rapid AI Prototyping Techniques
- Running playground feasibility spikes on messy real-world inputs
- Wizard-of-Oz, no-code flows and interactive prototypes for user testing
Minimum Viable Quality (MVQ) and the Discovery Eval Set
- Defining what 'good enough' means in measurable product terms
- Collecting a 30–50 example golden test set from real user workflows
Workshop: AI Feature Brief and Acceptance Criteria
- Defining problem scope, input/output contracts and fallback UX for a live case
- Delivering the AI feature brief and discovery eval plan
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