For aspiring AI PMs
Don't describe product judgment.
Show it.
The build-first route into AI product.
The fastest way into an AI PM role is a working AI product and the story behind it. Specky is the workspace for that story: your users, your spec, and every decision you made, in one place.
- User evidence you can cite
- Specs your build tool can use
- A case study that writes itself
Where Specky fits — and where it doesn't
Five stages, from choosing a problem to reaching out. Specky covers the product work — the first four. We'll tell you plainly where you need something else.
- 01 · Days 1–7
Pick a lane
Choose one domain and one specific customer problem you already know.
Specky does thisIdea Validation turns a one-line problem into target customer segments, the riskiest assumptions, and a plan to test them — so you commit to a lane with reasons, not a hunch.
- 02 · Days 8–30
Interview users, ship an MVP
Talk to ten users, then build one working AI product.
Specky does thisLog interviews in Research and let Specky synthesise pain points into findings you accept or reject. Draft a PRD that cites those quotes, then export it as a build prompt for v0, Bolt, or Lovable.
- 03 · Days 31–50
Evaluate
Measure task success, hallucinations, latency, and cost, plus human review.
Specky does thisGive each experiment an evaluation scorecard: start from the AI eval preset (task success, hallucinations, p95 latency, cost per task, human review), set targets, and record your results — Specky marks what passed and what missed. You run the evals in your own harness; Specky keeps the numbers next to the hypothesis and the trade-off decisions.
- 04 · Days 51–65
Show your work
Live demo, two-minute walkthrough, a one- to two-page case study, eval results.
Specky does thisStart from the AI Product Case Study template — problem, user quotes, approach, eval results, trade-offs, what didn't work — paste in your scorecard, and share it as a public link. You bring the demo and the walkthrough video.
- 05 · Days 66–90
Reach out
Five targeted contacts each weekday, each with a tailored note.
Not Specky's jobSpecky doesn't send outreach. What it gives you is the link worth putting in the note — a case study grounded in real users.
What you walk in with
Hiring managers ask why you built it, how you know users want it, and what didn't work. These answer all three.
A problem backed by quotes
Interview findings linked to the exact observations behind them. When the interviewer asks "how do you know?", you open the evidence.
A PRD with citations
Every requirement traces back to a user signal. It shows you can write a spec an engineer — or a coding agent — can build from.
An eval scorecard
Targets and results for task success, hallucinations, latency, cost, and human review — including the ones you missed and what you changed.
A decision log with trade-offs
Quality vs. cost, speed vs. accuracy, simple MVP vs. long-term vision — written down when you made the call, not reconstructed the night before.
A case study you can share
The AI Product Case Study template gives you the structure hiring managers expect. Publish it as a read-only public page for your application or outreach note.
Show what didn't work
- Assumptions you tested and killed, with the evidence that killed them
- Findings from interviews you rejected — and why
- Eval metrics that missed their target, next to what you changed
- Decisions you reversed once real users touched the product
Specky keeps rejected findings and invalidated assumptions instead of deleting them, so your iteration history is there when you need it.
What aspiring AI PMs ask
Why build a product instead of taking another course?
Because an AI PM interview is a judgment test. A certificate says you studied; a shipped MVP with user interviews, trade-offs, and failures says you can do the job. The build-first route gives the hiring manager something concrete to ask about.
Do I need to code?
Not much. Specky turns your spec into a ready-to-paste build prompt for v0, Bolt, or Lovable, or into tickets for a coding agent like Cursor or Claude Code. You own the problem, the users, and the decisions — the part the interview is actually about.
Does Specky run LLM evals on my MVP?
No — run them with whatever eval harness you like. Specky is where the results live: each experiment has an evaluation scorecard where you set targets for task success, hallucination rate, latency, cost, and human review, record what you measured, and see what passed. Copy it as a table straight into your case study.
Will Specky do my job outreach?
No. Specky doesn't send messages to recruiters or hiring managers. It gives you something worth linking in your note: a shareable spec or case study grounded in real user evidence.
What do I walk into the interview with?
A problem statement backed by interview quotes, a PRD with the evidence cited, an eval scorecard, a decision log showing what you chose and why, and a case study built from the AI Product Case Study template that you can share as a public link.
A working product gives you
a story worth interviewing.
Pick the problem. Talk to the users. Ship the thing. Specky keeps the evidence so the story is true when you tell it.