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📋Product
AI Product Manager
Ship AI Products That Actually Work in Production
Most PM courses teach you buzzwords. This program teaches you what happens when AI products meet real users. You will learn enough about LLMs, RAG, agents, and evals to lead engineering teams without being lost, define AI success metrics that go beyond accuracy, run user research for AI-specific failure modes, and ship features that users trust. Built around real AI product case studies: ChatGPT, Perplexity, Cursor, Notion AI, Bing Copilot.
Duration
10 weeks
Level
Intermediate
Starts
October 1, 2026
Modules
5 modules
What You'll Learn
LLM capabilities, limitations, and failure modes for PMs
RAG, Agents, and Fine-tuning — what to build vs. buy
AI product specs: from user need to model requirement
Evaluation-driven development: defining and measuring AI quality
User research for AI: trust calibration, mental models, error UX
Cost modeling: tokens, latency, infra — pricing AI features
AI roadmapping: managing uncertainty, iteration velocity
Regulatory & ethics: EU AI Act, GDPR, bias auditing
Full Curriculum
Detailed Syllabus
01
Weeks 1–2
AI Literacy for Product Managers
- How LLMs work: tokens, context windows, temperature, hallucination mechanisms
- RAG explained for PMs: when retrieval beats fine-tuning, latency implications
- Agents and tool use: what autonomous AI can and cannot do reliably today
- AI product taxonomy: copilots, chatbots, classifiers, generative features — choosing the right pattern
- Case studies: Cursor (AI code editor), Perplexity (AI search), Notion AI (embedded AI) — PM decisions behind each
- Lab: Prompt a production LLM 50 different ways; document failure modes and edge cases
02
Weeks 3–4
AI Product Discovery & Scoping
- Identifying AI opportunities: the jobs-to-be-done framework applied to AI
- AI feasibility assessment: what's possible today vs. 6 months vs. 2 years
- Writing AI product specs: user story → model requirement → evaluation criteria
- Build vs. buy vs. fine-tune decision framework for PMs
- Scoping AI MVPs: defining 'good enough' for v1 AI features
- Stakeholder alignment: engineering, legal, data, design — who owns what in AI projects
- Lab: Write a full AI product spec for a real use case (AI customer support / AI search)
03
Weeks 5–6
Evaluation-Driven Development for AI
- Why traditional A/B testing breaks for AI: distribution shifts, qualitative outputs
- AI evaluation taxonomy: automated evals, human evals, LLM-as-Judge
- Defining eval suites: golden datasets, regression tests, adversarial test cases
- Metrics that matter: TTFT (time-to-first-token), faithfulness, task completion, user retention
- Hallucination monitoring: detection strategies and user communication patterns
- Evaluation infrastructure: how to set up evals as a PM — tools (LangSmith, Arize, Braintrust)
- Lab: Define a complete eval suite for an AI feature; run 3 model variants; recommend a winner
04
Weeks 7–8
User Research & UX for AI Products
- AI-specific UX challenges: trust calibration, error recovery, progressive disclosure
- Designing for hallucination: citation UI, confidence indicators, graceful degradation
- User research methods for AI: think-aloud sessions, mental model mapping for LLMs
- AI onboarding: closing the gap between user expectations and model capability
- Accessibility and fairness: bias auditing, demographic parity, disparate impact testing
- Case study: How Bing Copilot, Claude.ai, and ChatGPT handle trust and error UX differently
- Lab: Run a 5-person user research session on an AI feature; synthesize findings into product decisions
05
Weeks 9–10
AI Cost Modeling, Roadmapping & Compliance
- Token economics: input/output costs, context window optimization, caching strategies
- Latency budgeting: streaming UX, perceived performance, when to show partial results
- Pricing AI features: per-seat vs. usage-based vs. freemium — case studies
- AI product roadmapping: managing uncertainty, iteration loops, model upgrade planning
- EU AI Act overview: high-risk AI systems, transparency requirements, conformity assessment
- GDPR for AI: data minimization, right to explanation, automated decision-making limits
- Capstone: Full AI product strategy — problem statement, spec, eval plan, roadmap, cost model, compliance checklist
Outcomes
After this program, you'll be able to:
- Speak fluently with AI engineers without getting lost in technical details
- Write production-quality AI product specs that engineering teams can execute
- Define eval suites and measure AI quality beyond accuracy metrics
- Run user research sessions designed specifically for AI product failure modes
- Build cost models for AI features and make build vs. buy decisions confidently
- Roadmap AI products that account for model uncertainty and iteration velocity
- Navigate EU AI Act and GDPR requirements for AI-powered features
Prerequisites
Before you start, you should have:
- 2+ years of product management or product design experience
- No coding experience required
- Curiosity about AI — you will be writing prompts and reading eval results
Ready to master AI Product Manager?
Download the full syllabus or chat with us on WhatsApp.