The AI Product Manager

Build AI products the right way. From model evaluation to responsible AI — the complete

PM framework for the age of LLMs.

What's inside the Playbook (PDF)

• How to scope AI features vs traditional software features

• Evaluating AI models: accuracy, latency, cost, and risk

• Responsible AI framework — bias, fairness, transparency

• Roadmap prioritization for AI-first products

• Writing effective AI product specs and acceptance criteria

• Building feedback loops to improve model performance

• How to communicate AI limitations to stakeholders

• AI product metrics: beyond accuracy to business impact

What's inside the Templates (Excel)

• AI Feature Scoping Canvas — use case, data, risk, effort

• Model Evaluation Matrix — compare providers and versions

• Responsible AI Checklist — pre-launch ethical review

• AI Roadmap Planner — capability vs experience layers

• Prompt Engineering Log — track prompts and performance

• AI Product Metrics Dashboard — quality, adoption, impact

Who this is for

• PMs building AI-powered features or products

• Founders launching LLM-based applications

• Product leaders evaluating AI vendors and platforms

• Technical PMs bridging engineering and AI strategy