1. Problem framing
Start with a concrete user decision to improve, not a model-first idea. Define what success looks like in behavior and business terms.
Playbook
A practical framework for shipping trustworthy AI products.
AI Playbook
How I Build AI Products
A practical five-step framework I use to ship AI features that are reliable, explainable, and actually useful.
2. Evals first
Build evaluation sets early: happy-path, edge-cases, and failure examples. Track quality before adding UX polish.
3. UX constraints
Design interaction boundaries: confidence signaling, fallback paths, and transparent state so users understand what the system is doing.
4. Safety and guardrails
Add policy checks, tool permission boundaries, and auditable traces. High-trust products need observable and controllable AI behavior.
5. Iterate with telemetry
Ship with instrumentation, review real sessions, and improve prompts/tools/evals in loops. Learning velocity is the moat.