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AI Product Management — The Complete Course

How to scope, build, ship, and measure AI-powered products. 20 chapters of frameworks, decision points, and real-world patterns.
Co-Created by Kiran Shirol and Claude
5 Acts Mindset Discovery Building Shipping Growth
home Learning Portal play_arrow Start Learning summarize Key Insights dictionary Glossary handyman PM Toolkit 20 chapters · Sequential or jump to any topic
Act I

The AI Product Mindset

What makes AI products fundamentally different from traditional software.
1
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Why AI Products Are Different
You ship confidence levels, not guarantees. The mental model shift every PM needs.
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2
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The AI Product Landscape
Where AI products actually work today — from enhanced features to AI-native products.
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3
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AI Product Roles & Team Structure
Who you need, how AI teams differ, and the PM’s relationship with data science.
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4
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The AI Product Lifecycle
Why AI products get better after launch — and can also get worse. The data-model-product loop.
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Act II

Discovery & Scoping

How to find the right problems and scope AI solutions correctly.
5
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Problem Framing for AI
The #1 PM skill for AI products. When to use AI and when not to.
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6
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Data Discovery & Feasibility
Before you build anything: do you have the data? The feasibility checklist every PM needs.
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7
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Build vs. Buy vs. API
Foundation model APIs, fine-tuning, custom models, or vertical SaaS? The decision matrix.
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8
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Writing AI Product Specs
What changes in a PRD when the product is probabilistic. The AI Product Requirements Canvas.
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Act III

Building & Evaluating

The build phase — what PMs need to know to lead effectively.
9
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Model Development for PMs
What happens between spec and model. What to ask in standups. When to push back.
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10
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Evaluation & Metrics That Matter
Accuracy is not enough. Precision, recall, F1 — what they mean in product terms.
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11
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Prompt Engineering as Product Design
For LLM products: prompts are your product logic. How to version, test, and iterate.
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12
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RAG & Knowledge Integration
When your product needs proprietary data. RAG architecture, chunking, and grounding quality.
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13
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UX Patterns for AI Products
Designing interfaces for probabilistic systems. 12 proven patterns with examples.
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Act IV

Shipping & Operating

Getting AI products to production and keeping them healthy.
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Testing AI Products
You can’t write deterministic tests for probabilistic systems. Evaluation-driven development.
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15
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Launch Strategy for AI Products
Staged rollouts, feature flags, and when to launch at 80% vs. waiting for 95%.
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16
monitoring
Monitoring & Observability
What to watch after launch. Model drift, cost per query, and when to retrain vs. rollback.
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17
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AI Product Operations
Token budgets, compute costs, incident response. The ongoing cost of running AI products.
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Act V

Growth & Governance

Scaling AI products responsibly and building for the long term.
18
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Measuring AI Product Success
Beyond traditional metrics. Task completion rate, automation rate, cost-per-outcome.
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19
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AI Product Ethics & Safety
Bias detection, content safety, user consent. When to add friction intentionally.
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20
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The AI Product Roadmap
How to roadmap when the technology changes quarterly. Building strategy that survives.
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