Phase 0: AI Product Management Foundations · 45 min · AI Product Lifecycle Map · Python

The AI Product Lifecycle

A PRD nobody can fault won't save a launch if eval and monitoring were never built — the lifecycle is one connected system, not a checklist you can skip around.

Hiring signal: Interviewers use 'walk me through how you'd take this AI feature from idea to production' as a default AI PM prompt because it exposes candidates who only know the parts of the lifecycle they've personally shipped. PMs who can name every stage, what evidence 'done' requires at each one, and where a feature is actually stuck are the ones trusted to own a launch end-to-end rather than just the parts that feel comfortable.

What you will learn

Name every stage of the end-to-end AI product lifecycle, from opportunity through post-launch monitoring
Map each lifecycle stage to the specific later phase of this course that builds the skill for it
Explain why skipping or rushing any single stage (especially eval and monitoring) creates risk that shows up later, not immediately
Given a failed AI feature, diagnose which lifecycle stage was actually skipped versus which stage merely surfaced the failure
Describe what artifact or evidence counts as 'done' at each lifecycle stage

The Problem

A PM at a mid-size fintech spends three weeks on a PRD for an AI-powered spending-insights assistant. It's genuinely excellent: clear user stories, a tight scope, explicit non-goals, a cost budget per query, even a section on tone and disclaimers. Engineering builds against it almost without friction. The demo two weeks later is great. Everyone claps.

Six weeks after launch, the feature is quietly disabled. Not because the PRD was wrong — because nobody on the team had defined what "correct" meant for a non-deterministic output before it shipped, so there was no eval suite to catch it drifting into confidently wrong answers about a user's own transactions. And nobody had wired up production monitoring, so the team's first signal that anything was broken was a spike in support tickets and a churn-risk flag from a large customer, three weeks after the regression started. The PRD stage was flawless. The eval stage and the monitor stage didn't exist. The feature failed anyway.

This is the single most common failure mode in AI product work, and it's a lifecycle failure, not a document failure. Traditional software PMs can often get away with treating stages as a checklist — write the spec, ship it, move on, and if something breaks, the bug tracker catches it because bugs are usually deterministic and reproducible. AI features don't fail that way. They degrade statistically, on inputs nobody tested, in ways a bug tracker doesn't catch and a well-written PRD doesn't prevent. The only defense is treating opportunity, data, PRD, eval, build, responsible AI review, launch, and monitoring as one connected system where a gap at any stage becomes a failure at a later, more expensive stage.

This lesson is the map. Every later phase of this course builds deep skill in one stage of it — this lesson previews all of them so you know where you are, and where you're weak, at any point in a real project.

The Eight Stages

Stage	Course phase	What a PM actually produces
Opportunity	Phase 2 — Discovery & AI Product Strategy	An opportunity assessment: build/buy/fine-tune call, feasibility and risk triage, a roadmap slot (Project 1)
Data	Phase 3 — Data Strategy for AI Products	A data audit, a labeling/annotation plan, a feedback-loop design, a privacy/governance review
PRD	Phase 4 — Writing AI Product Requirements	An AI PRD: scoped behavior spec, prompt spec, explicit non-deterministic-behavior bounds, failure-mode design, cost/latency budget (Project 2)
Eval	Phase 5 — Evaluation & Quality for AI Products	A rubric, an eval set, an LLM-as-judge or human eval pipeline, offline results, a ship/no-ship recommendation (Project 3)
Prompt / Build	Phase 6 — Prompt Engineering & GenAI Feature Design	Working prompts, structured outputs or tool-calling specs, a prototype validated against the eval set
Responsible AI review	Phase 7 — Responsible AI, Governance & Risk	A bias/fairness check, a transparency and disclosure plan, an incident-response plan (Project 4)
Launch	Phase 8 — Launch, Metrics & Scaling AI Products	A rollout plan (canary/staged), defined AI-specific KPIs, a go/no-go gate
Monitor	Phase 8 — Launch, Metrics & Scaling AI Products	Drift dashboards, unit-economics tracking, a retraining/re-prompting trigger

Two things worth noticing in that table. First, Opportunity and Data come before you write a word of PRD — a PRD written before you know whether the data exists to support the feature is fiction with formatting. Second, Launch and Monitor are the same phase of this course on purpose: in AI products, launch isn't an event, it's the start of a measurement period. A "launched" feature with no monitoring isn't actually done — it's an unmonitored bet.

Every stage produces evidence, not just a document
It's tempting to read "PRD stage" as "write the PRD document" and consider the stage complete. The actual definition of done at each stage is evidence that a specific question was answered — not that a document with the right section headers exists. A PRD is done when it specifies what the model should do when it's uncertain, not just what it should do when it works. An eval stage is done when you have a scored result on a held-out set, not when you have a rubric sitting in a doc nobody ran. Treat every artifact as a proxy for a question — if you can't say what question it answers, it isn't evidence, it's paperwork.

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You've read the first 2 sections. The rest of this lesson covers Why the Order (Mostly) Matters, Using the Map to Diagnose, Not Just Plan, Build It, What to Practice — plus a hands-on lab, quiz, and project artifact.

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