AI Agent Workflow Collection

Evaluate AI capability sourcing options across build, buy, fine-tune, and partner archetypes using a structured decision matrix. Use when deciding whether to build a custom model, use an LLM API, fine-tune a base model, or partner with an AI vendor.

Define a data strategy for AI products covering training data requirements, data quality, labeling strategy, feedback loops, and continuous retraining. Use when assessing data readiness for an AI feature, designing a data flywheel, or planning an ML data pipeline.

Write a complete AI feature specification that defines desired model behaviour, input/output examples, confidence thresholds, fallback logic, and non-functional requirements. Use when defining requirements for an AI-powered feature, writing an AI PRD, or bridging the gap between PM intent and ML team implementation.

Evaluate and compare LLMs, ML APIs, and fine-tuned models for product fit across quality, latency, cost, compliance, and vendor risk dimensions. Use when selecting an AI model or vendor, comparing foundation model options, or making build-vs-API decisions for a product use case.

Research user expectations, mental models, trust calibration, and interaction patterns for AI-powered features. Use when planning user research for an AI feature, evaluating user trust and satisfaction with AI outputs, or designing feedback collection for a machine learning system.

Review AI-generated code from a PM perspective: spec compliance, security red flags, data model soundness, UX issues in frontend code, and error handling against product requirements.

Guide PMs through debugging AI-generated code: how to read error messages, form effective debugging prompts, isolate issues, and decide when to fix forward vs start over.

Interactive Socratic learning module on AI product management: model evaluation, responsible AI, prompt engineering, AI metrics, and managing AI features in production.

Capstone project for the PM Foundations Course: act as PM for a product from discovery through launch decision, applying every framework learned across all prior modules in a single integrated case study.

Interactive learning module on product execution: writing PRDs, building outcome-focused roadmaps, setting OKRs, running sprint planning, and managing delivery without micromanaging engineers.

Interactive introductory module covering PM fundamentals: the PM role, core mindset, key frameworks overview, and the PM skill tree — designed for those new to product management.

Interactive metrics workshop teaching North Star metrics, input metrics, and counter-metrics through a scored, Socratic exercise with a fictional B2C app scenario.

PM Foundations Course coordinator: enroll in the structured 11-module PM learning curriculum, track your progress through units, and get personalized guidance on your next step in the course.

Interactive prioritization framework module where learners apply RICE, ICE, and Opportunity Score to a realistic product backlog and learn when each framework is most useful.

Multi-stakeholder simulation where the AI plays conflicting executives and the learner practices roadmap alignment, negotiation, and coalition-building as a fintech PM.

Guided learning module on product strategy using Roger Martin's Playing to Win cascade, applied through a realistic B2B case study.

Simulated user interview practice module where the AI plays a realistic user persona and provides structured coaching feedback on interview technique, question quality, and insight extraction.

Interactive guide for PMs getting started with vibe coding: choosing and installing the right AI coding tools, writing your first spec, planning a build, and shipping a prototype.

Define and distinguish operational, vanity, and actionable metrics. Write complete metric specs with name, definition, data source, calculation formula, owner, and review cadence. Use when formalizing metrics, building a metrics dictionary, or aligning a team on how metrics are measured.

Brainstorm 3-5 monetization strategies with audience fit, risks, and validation experiments.

Structure an evidence-based pivot, persevere, or stop decision using Eric Ries's Lean Startup pivot framework — evaluates current metrics against original hypotheses and scores pivot options against real data. Use when metrics consistently miss targets, customer feedback contradicts assumptions, at planned evaluation milestones, or when the team debates direction.

Validation group skill. Plans a runnable prototype that retires the top assumptions in a realistic environment. Differs from MVP by being internally-scoped (limited audience, throwaway by default). Includes demo script. Produces poc-plan.md and a runnable skeleton scaffold. Reads discovery-context.md.

Create refined user personas from research data — 3 personas with JTBD, pains, gains, and unexpected insights.

Assess an AI feature or product for ethical risks, bias, safety issues, fairness gaps, and regulatory compliance. Use when reviewing an AI feature before launch, conducting a responsible AI audit, or responding to a bias or safety concern.