AI PM learning without the noise
A practical AI PM system for turning uncertain product questions into defensible decisions—across discovery, data, models, evaluation, launch, and scale.
3 complete chapters · free account · no card
01Decision outcomes
The curriculum starts with the question in front of you. Frameworks and tools are supporting evidence—not the point of the learning.
Decision ledger Live
| Input (noisy) | Decision (resolved) | Status | ||
|---|---|---|---|---|
| 01 | Build an AI assistant for users | Problem worth solvingUsers waste 3+ hrs/week on manual info synthesis. | Resolved | |
| 02 | We should collect more data | Data requiredUser tasks, time-on-task, errors, and decision quality. | Resolved | |
| 03 | Might not get adoption | Failure modeNo clear ROI in first 14 days; workflow disruption. | Resolved | |
| 04 | Measure engagement maybe? | Eval metricTime saved per task, decision quality lift. | Resolved | |
| 05 | Lots of stakeholders, different opinions | Stakeholder tradeoffSpeed vs. accuracy vs. control. Start narrow, prove value. | Resolved | |
02Inspect the work
The AI Feasibility Matrix forces a decision before an engineering sprint begins: proceed, validate with a prototype, choose a simpler approach, or stop.
Read the free chapter →Should this product problem use AI at all?
Is ambiguity central enough to justify AI?
Score 1–5Does usable, legal, relevant data exist now?
Score 1–5Can quality be measured before launch?
Score 1–5What happens when the system is wrong?
Score 1–5Academic papers and primary industry material anchor Further Reading.
Important claims identify whether evidence is established or still emerging.
Chapter metadata records when the material was last checked.
Retrieval checks connect chapters to toolkit artifacts and case studies.
03The focus aperture
Turn Focus on in the live preview. The active idea stays sharp while navigation and adjacent context recede—without hiding your place or making the mode automatic.
Manual focus · contextual concepts · saved progress
Open the reader →A model can be replaced. The product learning loop around it is much harder to copy.
The product decision is not simply what data to collect. It is which feedback creates a compounding advantage—and which data should never enter the loop.
That means setting consent, quality, ownership, and retention boundaries before scale.
04Complete curriculum
Thirty-one chapters move in sequence from problem choice to operating and scaling AI products. Start with the decision you need now, or follow the entire path.
Decide whether the problem is worth solving and why AI belongs.
Set data, model, experience, and trust boundaries.
Define quality, cost, latency, safety, and failure gates.
Price, ship, learn, and scale with explicit tradeoffs.
Choose a phase to reveal its chapters
Build product judgment before committing to a model or roadmap.
05Meykai membership
One Meykai membership. Choose monthly flexibility or the best-value annual plan; both unlock the complete released library.
Annual membership
7-day free trial$149$100/year
Founding price: 33% below the $149 annual reference price and 44% below twelve monthly payments.
Planned price: $100/year. No payment or trial starts while checkout is closed.
No payment details are collected while checkout is closed. Digital delivery only; nothing is physically shipped. Terms · Delivery · Cancellation and refunds
FAQ
Product managers moving into AI work and PMs already responsible for AI features or products. You do not need to be an ML engineer, but familiarity with product fundamentals will help.
The released path includes working artifacts for feasibility, AI requirements, data strategy, model evaluation, cost analysis, launch readiness, and stakeholder decisions.
Chapters expose verification dates, epistemic-status labels for important claims, Further Reading, retrieval checks, and links to the relevant tools and case studies. The source trail stays visible so the material can be challenged and corrected.
Yes. Create a free account to read the first three chapters in the actual Meykai reader. Paid memberships are not open yet. Create a free account to read the first three chapters. We’ll announce checkout when payment verification is complete.
These are the planned launch terms: 3 days for monthly and 7 days for annual membership. Paid memberships are not open yet. Create a free account to read the first three chapters. We’ll announce checkout when payment verification is complete.
Meykai is self-paced. Every chapter shows an estimated reading time, so you can follow the full sequence or start with the decision you need to make now.
No. The current offer is a self-paced working library with released chapters, tools, case studies, search, saved progress, and the manual Focus reader.
Start with evidence
Open Chapter 1 in the actual reader. Follow the source trail, inspect the learning method, and decide if the depth is right for you.
3 complete chapters · free account · no card