Notion
Plan & explain
Keep a project brief, decisions and supporting evidence in a shared document.
All capabilities · Product, business & communication
Discover and scope AI product opportunities
Identify where models add value, assess feasibility/data readiness, write an AI PRD with success metrics.
Explore 4 tools for this projectMarket relevance
Which roles ask for this — and how often
Share of job postings in India, per role, that name this capability.
What employers mean
You should be able to…
- Run customer discovery sessions and translate business problems into scoped AI product capabilities
- Assess feasibility and data readiness before committing a roadmap slot to an AI feature
- Write a PRD with explicit success metrics, not just a feature description
- Prioritize which AI capability to build first given engineering cost and business impact
- Evaluate build-vs-buy: off-the-shelf LLM API vs fine-tuned model vs vendor product
- Define the launch bar — what 'good enough to ship' means for a probabilistic feature
- Say no to an AI feature request and explain the reasoning to stakeholders
Needs first: Explain how LLMs work and where they fail
Learn — free, link-checked
The few resources that matter
Read · beginner · 25 min · atlassian.com
Product requirements: how to write a great PRD
A concrete, widely-used PRD template and structure you can adapt for an AI feature's success metrics. — Atlassian
Read · beginner · 25 min · tryexponent.com
What is an AI Product Manager?
Maps the AI PM role to concrete responsibilities (feasibility, data readiness, eval) recruiters actually screen for. — Exponent
Course · beginner · 60 min · cloudskillsboost.google
Introduction to Generative AI
Vendor-neutral, structured primer on what GenAI is and how it differs from traditional ML — good for a first pass. — Google Cloud Skills Boost
Course · beginner · 240 min · deeplearning.ai
Generative AI for Everyone
Andrew Ng's free, non-technical framing of where GenAI creates business value — exactly the PM lens you need. — DeepLearning.AI (Andrew Ng)
Tools for practice
Choose a tool for the job
Start with one tool for each part of your project. You don’t need to learn them all.
4 tools to explore
Figma
Plan & explain
Make a clickable flow to test a product idea before building the application.
Google AI Studio
Build
Test prompts and model inputs before turning a feasibility experiment into code.
Amplitude
Data · Test
Explore product events and funnels to turn usage into a testable product question.
Practices & references
- Problem interviews
- Feasibility experiments
Practice
AI feature PRD: automated GST-invoice extraction for an SMB accounting tool
Talk to three people who re-key invoices by hand — a shop owner, a freelancer's accountant, a friend in accounts payable — or role-play them from a written persona if you cannot reach three. Then write a full PRD for a feature that extracts structured fields (GSTIN, amount, tax breakup) from a photographed invoice. Test feasibility yourself first: photograph 20 invoices you already have and hand-check what a vision model gets right. The PRD needs success metrics, a numeric launch bar, and a phased rollout with a human-review queue for low-confidence extractions.
Start from
Three discovery conversations you run yourself with people who key invoices by hand, plus 20 invoices you photograph from your own receipts
Milestones
- Write the persona and interview guide, then run all three conversations · ~3h
- Feasibility spike: photograph 20 invoices and hand-check what a vision model extracts · ~2.5h
- Draft the PRD with success metrics and a numeric launch bar · ~2.5h
- Add the phased rollout and the low-confidence human-review fallback · ~2h
Done when
- PRD includes problem statement, at least 3 real or role-played user quotes, and explicit success metrics
- Feasibility section names specific data sources and a realistic accuracy target with justification
- A launch bar is defined (e.g. '95% field-level accuracy on a held-out set of 200 invoices before GA')
- Rollout plan includes a fallback for low-confidence extractions (human review queue)
Prove it
Evidence a recruiter can check
- A PRD whose launch bar is a number on a held-out set ('95% field-level accuracy on 200 invoices before GA'), not a feeling
- Feasibility notes showing what a vision model actually extracted from 20 invoices you photographed, and which fields it lost
- Three sets of interview notes with the verbatim quotes that changed your scope
- A one-slide build-vs-buy call with the assumption it rests on stated out loud
Signal it
Scoped an invoice-extraction feature end to end — three user interviews, a hands-on feasibility test on 20 real invoices, and a PRD with a numeric launch bar and a human-review fallback.
Interview
Questions you'll get asked
- Walk me through how you'd scope an AI feature from a vague 'use AI for X' request.
- How do you assess whether we have the data to make an AI feature work before writing a single line of code?
- What goes into your AI feature PRD that wouldn't be in a normal PRD?
- Tell me about a time you said no to an AI feature — what was the reasoning?
- How would you prioritize between an AI feature that saves internal ops time vs one that's customer-facing?
- What's your process for deciding RAG vs fine-tuning vs a third-party API for a new capability?