AI Prototyping

This guide teaches practitioners how to build effective AI prototypes through a structured, 12-step execution pipeline. Rather than creating impressive demos, it emphasizes treating prototypes as "learning and discovery instruments designed to surface risk early." The methodology covers user triggers, hypothesis definition, prototype selection, orchestration logic, model invocation, output validation, evaluation metrics, cost measurement, failure testing, telemetry capture, and decision gates.

The content walks through a real AI Research Assistant prototype example, showing each step with actual screenshots and logging outputs. It targets product managers, founders, and engineers who understand basic AI concepts but need systematic frameworks for moving beyond "prompting until it looks good," emphasizing that AI behavioral risk differs from traditional software feasibility concerns.