THE LIBRARY

Ideas worth taking into the room.

Practical playbooks for the role. Perspectives on the decisions behind AI products. Follow your curiosity, or find the piece you need for your next interview.

Find your learning path

33 resources to explore

AI PM / INTERVIEWS

GitHub study resources

Playbook11 min read

GitHub Resources for AI PMs: Turn Repositories into Interview Evidence

Seven repositories. A product decision to practise with each one.

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AI PM / INTERVIEWS

Role & responsibilities

Playbook5 min read

What is an AI Product Manager?

The job, its variants, and the decisions only this PM makes.

AI PM vs Product Manager vs ML PM: what actually changes

Same craft, different definition of done.

How to Become an AI Product Manager: a practical roadmap

What to learn, in what order, and how to prove it.

AI Product Manager Skills: what interviews actually test

Eight skills, and the line between a weak and a strong answer.

The AI PM Interview Process, round by round

Each conversation, what it tests, and how to prepare.

Companies hiring AI product managers, and how each type interviews

Four kinds of employer, four different loops.

AI Evals for Product Managers: a working guide

Define good, measure it, decide when it is enough.

AI Product Sense: a worked interview answer

One prompt, answered step by step, with the scoring logic.

Why Strong PMs Fail AI PM Interviews

Eight patterns, and the answer that replaces each one.

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AI PM / INTERVIEWS

interview preparation

Insight5 min read

AI PM Interviews in October 2026: Prepare for the Product You Will Own

A dated review of employer guidance and candidate accounts, with practice for model platforms, offline AI and enterprise product decisions. See what the evidence supports and what to confirm with your recruiter.

The PM Questions a Better Model Cannot Answer

Practise customer choice, estimation, metric diagnosis and distribution with original AI product cases. Strengthen the PM reasoning behind your technical interview answers.

The Technical AI PM Interview: Follow One Request End to End

Practise technical judgment by tracing an unread-message summary from permissions and context to model behavior, evaluation and cost. Includes a study route and a whiteboard exercise.

What an AI Demo Must Prove Before It Becomes a Product

Follow a maintenance assistant from a promising demonstration to a defensible investment. Test demand, data readiness, operating cost and ownership with original practice cases.

Defend Your AI PM Portfolio with Evidence

Prepare a project story a reviewer can inspect: your decisions, permitted artifacts, metric definitions and the limits of your evidence. Includes a practical claim-audit worksheet.

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AI PM / INTERVIEWS

interview preparation

Insight4 min read

Build an AI PM Interview Plan from Public Evidence

Use official interview guides and AI PM role descriptions to choose useful practice. Six original exercises cover writing, evals, model behavior, voice, experiments and agent recovery.

How to Defend an AI Launch Decision in an Evals Conversation

Turn an AI quality score into a launch decision. A worked contract-summary exercise covers sample size, failure severity, segment floors and production monitoring.

The Cheaper Model Can Cost You Nearly Three Times More

Why dividing an AI bill by requests misleads product decisions, with a worked comparison of a small and a mid-tier model once failures and human fallback are counted.

What Klarna's Support Assistant Teaches AI PMs About Automation Rates

Klarna's support figures and disputed quality narrative show why AI PMs need source checks, segment metrics and a measure of resolution that holds.

When 'Resolved' Is the Invoice: Outcome Pricing for AI Agents

Per-resolution pricing turns a metric definition into a billing term. Using Fin's $0.99 model as the example, what a PM must decide about abandonment, re-contact and multi-issue conversations.

Read the Definition Before the Number: AI Metrics That Change Meaning

When Salesforce widened what counts toward Agentforce ARR, the headline nearly doubled in two quarters. How AI PMs should define, change and report metrics so the trend means something.

The AI Regulation Dates Your Product Roadmap Needs, as of November 2026

EU AI Act milestones after the 2026 Digital Omnibus, the US state laws taking effect, and India's DPDP rules, laid out as a calendar a product manager can plan against. Not legal advice.

Prepare This Story First: The AI Launch You Stopped

Why a launch you stopped, delayed or narrowed is the most useful behavioural story an AI PM can bring, how to structure it with evidence, and the follow-ups that test whether it is true.

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AI PM / INTERVIEWS

Insight4 min read

What the Bartz v. Anthropic Settlement Means for AI Product Decisions

A $1.5 billion settlement over books obtained from pirated sources excludes model-output claims from its release. What that leaves for AI PMs to decide about data provenance, indemnities and output risk.

Agentic Protocols for Product Managers

MCP, A2A, agent interfaces, approvals, agentic commerce and payments, explained as the decisions a product manager owns, with a ten-worksheet toolkit for the PRD.

Autonomous ADLC for Product Managers

PRDs and specifications agents can build from, acceptance evidence a builder cannot weaken, delegation by action, release gates and cost per accepted change, with a ten-worksheet toolkit.

Harness Engineering for Product Managers

Loops, instructions, context, tools, verification, permissions, credentials, cost and evals, framed as the decisions a product manager owns, with a ten-worksheet toolkit for the harness PRD.

Agentic Database for Product Managers

Authority, permissions, retrieval, memory, knowledge layers, tools and recoverable actions, framed as the data decisions a product manager owns, with a ten-worksheet toolkit for the data PRD.

Enterprise Personal Agents for Product Managers

Jobs and autonomy, delegated access, approvals, prompt injection, memory, notifications, value and governance for agents that work for one employee, with a ten-worksheet toolkit for the agent PRD.

Systems Engineering for Product Managers

Outcome contracts, the least machinery that works, data contracts, capacity, latency budgets, evaluation with intervals, releases, offline mode and handoff, with a ten-worksheet toolkit for the deployment PRD.

AI Foundations for Product Managers

Make product decisions with a working understanding of models, retrieval, agents and evaluation. Build a feature brief with test cases, economics and recovery design, then practice explaining your choices. Includes a transition route for engineers and data scientists.

Cracking the AI Product Manager Interview

Practice product sense, technical discussion, evaluation, economics, system design, execution and stakeholder judgment. Follow worked cases, complete a scored take-home and defend your answers under changing constraints.

Building & Scaling Agentic AI Products

Lead an agent product from investment decision to operating service. Design authority and recovery, build an evaluation program, defend pricing and adoption, and present a coherent plan across product, security and finance.

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