Phase 9: Career, Portfolio & Interview Prep · 160 min · Python · Portfolio case study assembly

Project: Full AI Product Case Study (Capstone)

Nine phases, four projects, one feature. This is the document a hiring manager actually reads.

Hiring signal: This is the single artifact the entire course has been building toward: a project-led portfolio piece research identifies as the highest-leverage way to demonstrate AI PM judgment to a hiring manager, assembled from real work across opportunity assessment, PRD, evaluation, and responsible AI review rather than four disconnected exercises. It is precisely the kind of coherent, end-to-end case study that lets a candidate skip a generic screen and go straight into a substantive conversation about their own actual work.

What you will learn

Assemble Projects 1-4 (opportunity assessment, PRD, eval plan, responsible AI review) into one coherent portfolio case study for a single feature
Write a narrative arc a hiring manager can read start to finish in one sitting, connecting each project's findings to the next
Build a mock interview presentation outline that walks through the case study's key decisions and tradeoffs
Present judgment and tradeoffs, not just deliverables, as the core evidence of AI PM readiness

The Problem

You've spent this course carrying one feature through four separate, serious pieces of work: an opportunity assessment that sized the bet and made a build/buy/model-selection call (Phase 2), a full PRD that specified probabilistic behavior, guardrails, and a cost model (Phase 4), an evaluation plan with a rubric and sample scoring (Phase 5), and a responsible AI review with a consequence-scan, regulatory classification, and guardrail decisions (Phase 7). Each one, on its own, is solid evidence of a specific skill. Stapled together as four separate PDFs in a folder, they're not a portfolio — they're homework.

The gap between "four completed exercises" and "the single artifact a hiring manager actually wants to see" is exactly what Lesson 1's research pointed at: project-led portfolios work because they let someone evaluate real judgment directly, but that only works if the judgment is visible as one continuous story, not four disconnected files a reader has to mentally stitch together themselves. This capstone is that stitching — and it's deliberately the last thing you build in this course, because it's the first thing a hiring manager will actually read.

What "Assembled" Actually Means

Assembly is not compression — it's not fitting four documents into fewer pages. It's building a narrative arc where each project's output becomes the input to the reasoning in the next, made explicit on the page:

Opening: the opportunity, in one paragraph — what the feature is, who it's for, and the specific number or finding from your Phase 2 opportunity assessment that justified pursuing it (not "AI seemed promising," but the actual sizing logic and the build/buy/model-selection call you made and why).
The PRD's key decisions, not the full document — pull 2-3 decisions that actually show judgment: how you specified acceptable probabilistic behavior, the guardrail you chose and the tradeoff behind it, the cost projection and what it constrained. Link to or attach the full PRD as backup; the case study itself shows the reasoning, not the whole spec.
The eval plan's real teeth — the rubric you built, one worked example of scoring an actual output against it, and (this is often the most differentiating part of a portfolio) an honest account of a borderline case your rubric didn't resolve cleanly at first, and what you changed.
The responsible AI review's headline finding — the consequence-scan's most significant finding, the regulatory classification, and the overall ship/mitigate/redesign call, stated with the same directness Phase 7's overall_recommendation() produces — a real decision, not a hedge.
Closing: what you'd do next — the honest state of the feature if it were real (would you actually ship it as specified, what would you want to validate first, what's the single biggest open risk) — this is where you demonstrate you understand the work isn't "done," it's at a specific, articulable stage.

The connective tissue between projects IS the evidence, not the projects themselves
A hiring manager who reads "the opportunity assessment identified self-employed applicants as underserved by existing underwriting; the PRD's guardrail decision specifically routes low-confidence scores for that segment to mandatory human review; the eval rubric includes a per-segment scoring pass specifically because of that guardrail; the responsible AI review's ethical KPI directly measures whether that guardrail is working" is reading a single, coherent judgment chain across four phases of work. A hiring manager who reads four summaries that never reference each other is reading four homework assignments. The explicit cross-references are the actual differentiator — go back through your four projects now and find at least three real connections like this before you write the narrative.

Two candidates both submit case studies covering the same kind of AI feature. Candidate A's case study is 20 pages long and covers every detail of all four projects exhaustively. Candidate B's case study is 9 pages, covers the same four projects, but every section explicitly states what alternative was considered and rejected and why. Which is the stronger portfolio artifact, per this course's standard, and why?

This course's case-study standard (stated in the lab rubric) explicitly favors documents that surface judgment and tradeoffs over documents that are merely exhaustive. A hiring manager skimming a portfolio is looking for evidence of decision-making — what was considered, what was rejected, and why — not maximum word count. Candidate A's 20 pages could easily be dense description with no visible reasoning; Candidate B's 9 pages, if every section names a rejected alternative and its rationale, demonstrates exactly the judgment an AI PM interview is designed to test. Length is a proxy for neither quality nor rigor on its own.

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You've read the first 2 sections. The rest of this lesson covers Building the Mock Interview Presentation, Build It, What to Practice — plus a hands-on lab, quiz, and project artifact.

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