aipc slides-2
This slide deck is part of the Artificial Intelligence Product Certification (AIPC) program and covers three consecutive modules. Module 3 focuses on building a Generative‑AI Product Requirements Document (PRD) with a deep dive into model selection, presenting trade‑offs between performance, cost, size, latency, specialization versus versatility, and open‑source versus proprietary models, plus an example PRD table of model requirements. Module 4 shifts to designing AI‑native user experiences, teaching product managers how to translate LLM capabilities into user‑flows, apply six UX best‑practice pillars (trust & verify, fail gracefully, guided onboarding, guided prompting, provide value, double‑check bias), and construct decision memos and wireframes. Module 5 addresses growth through non‑deterministic experimentation, outlining how to measure LLM‑powered products via human rubrics, automated metrics, and user feedback, and describing three core experimentation levers—prompt engineering, data, and model selection—along with best‑practice guidelines for A/B testing, over‑fitting avoidance, and documentation. Throughout, the deck includes case studies (e.g., Tinder hackathon, Google Gemini, Jasper AI), breakout exercises, templates, and resource links to reinforce practical application for product managers building and iterating generative‑AI features.
Topics
Model Selection Trade‑offs for Generative AI Products
A systematic comparison of key dimensions that influence LLM choice—including performance vs. cost, size vs. latency, specialization vs. versatility, and open‑source vs. proprietary models—presented with tables and bullet‑point analyses to guide product managers in selecting the right model for their generative‑AI feature.