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N1· Engineering: product, sales and operations roles

AI Product Management

You learn to find a real seller problem, write a spec an engineer and a model can both follow, prototype it with AI in an afternoon, and prove it works with an eval plan.

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Who it is for
Engineering students from any branch who would rather decide what gets built, and why, than write all of the code themselves.
AI core
9 units · 20 h
This course
6 modules · 12 lessons · 3.1 h
Live projects
2, on Professional and Career
01Roles it prepares you for
  • Associate Product Manager
  • Product Analyst

The skills these roles ask for. Preparation, not a promise of a job.

02Tools you will use
  • LovableFree tierVerified
  • v0Free tierVerified
  • Figma (Starter, with AI credits)Free tierVerified
  • Mixpanel (Free plan)Free tierVerified
  • PostHog (free tier)Free tier
  • Gemini Notebook (formerly NotebookLM)Free tier
  • Saarthi sandbox (anonymised seller data pack + MCP server)Saarthi sandbox

You never pay for a tool subscription. “Verified” means we checked the free access on the vendor’s own site this month. Tool names describe what you learn; no vendor endorses Launchpad.

03Syllabus

What you will learn, in order.

Lesson films are narrated with an AI voice, over visuals made with AI tools and checked by our team. Saarthi, the coach, is an AI. Mentors and reviewers are people.

Part 1 · The AI core, shared by every stream

00Set up your coach1 hLearn how the core works, write the learning-context document that tells every AI tool who you are, and set Saarthi up as a coach that makes you think instead of thinking for you.
  1. How this core works6 min
  2. Write your learning-context document8 min
  3. Seven roles for AI, and study modes7 min
01How LLMs work and fail2.5 hBuild a working mental model of a large language model: tokens, prediction, context windows and memory. Then learn its predictable failures (hallucination, sycophancy, stale knowledge, bad arithmetic) and reproduce them yourself.
  1. Tokens and next-word prediction7 min
  2. Context windows and memory7 min
  3. Hallucination, sycophancy and other failures8 min
  4. Lab: one prompt, three models, five failures8 min
02Description and context engineering3.5 hWrite briefs that get the right output the first time: role, task, context, examples and format. Then make them reusable with Claude and ChatGPT Projects, Gemini Gems, custom GPTs and NotebookLM, and learn to work with PDFs, spreadsheets and images.
  1. The brief: role, task, context, format8 min
  2. Examples and output formats7 min
  3. Projects in Claude and ChatGPT8 min
  4. Gems, custom GPTs and NotebookLM8 min
  5. Working with files: PDFs, spreadsheets, images8 min
03Verify everything2.5 hCheck AI output the way fact-checkers do: lateral reading, tracing citations and numbers to the original, using deep-research modes without trusting them blindly, and testing a prompt properly with a 10-case golden set.
  1. Lateral reading7 min
  2. Checking citations and numbers8 min
  3. Deep-research modes: use and limits7 min
  4. Your first eval: a 10-case golden set8 min
04Connectors and MCP2 hConnect AI to the places your work lives (Drive, Gmail, Sheets, business data) without writing code. Learn what an MCP server is, why a reviewed directory differs from unreviewed community servers, how to grant the least access that works, and how to keep browser extensions on a short leash.
  1. What connectors and MCP are8 min
  2. Trust, least privilege and the reviewed directory8 min
  3. Browser extensions and AI browsers7 min
  4. Lab: ask the seller-data MCP server, check every answer8 min
05Agents and automations3.5 hHand multi-step work to AI safely. Tell an agent from an automation, decide what to delegate, use browser and coding agents with care, and build an n8n workflow that uses AI with a human-approval step before anything leaves the system.
  1. Agents vs automations: what to delegate8 min
  2. Browser agents, safely7 min
  3. Automation basics with n8n and Zapier8 min
  4. Lab: build an automation with a human-approval step8 min
  5. Failure modes and your failure-mode note7 min
06AI for learning and careers1.5 hUse AI to learn faster without outsourcing the learning, write applications that still sound like you, and practise interviews out loud with Saarthi.
  1. Learning with AI, not instead of learning7 min
  2. Applications in your own voice7 min
  3. Mock interview with Saarthi8 min
07Diligence: law, ethics, integrity2.5 hThe rules that apply when you use AI in India: the DPDP Act 2023 and its 2025 Rules, academic integrity and disclosure, copyright (including where Indian law is still unsettled), and bias. You finish by writing your own AI-use policy.
  1. DPDP Act 2023: personal data and AI8 min
  2. Academic integrity and disclosure7 min
  3. Copyright and AI in India8 min
  4. Bias and fairness7 min
  5. Write your personal AI-use policy7 min
08Core capstone1 hPut all four Ds together on one anonymised marketplace settlement file: ask good questions, automate one step, verify every number against Saarthi's P&L engine, and defend your work live in five minutes.
  1. The capstone brief6 min
  2. Lab: ask, automate, verify8 min
  3. Prepare your 5-minute defence7 min

Part 2 · AI Product Management

01The PM job when AI writes the first draft2 lessonsWhat a product manager actually owns, how AI changes the work, and where it quietly misleads you.
  1. What a PM owns: the problem, not the feature8 min
  2. Lab: from seller data to three problem statements20 min
02User research and synthesis2 lessonsInterview sellers without leading them, and synthesise transcripts and reviews with AI without losing the truth.
  1. Interviewing sellers so they tell you the truth8 min
  2. Lab: synthesise interviews and reviews in Gemini Notebook20 min
03PRDs and specs for AI features2 lessonsWrite requirements that engineers can build and that define how an AI feature may and may not behave.
  1. Anatomy of a PRD for an AI feature8 min
  2. Lab: draft, attack and tighten a PRD20 min
04Prototyping with AI2 lessonsTurn a spec into a clickable prototype in hours, then test it with a real user before anyone writes production code.
  1. Prototypes are questions, not products8 min
  2. Lab: build and test a profit-leak prototype25 min
05Evals and metrics for AI features2 lessonsProve an AI feature works before launch with a golden set, and know after launch whether it is helping.
  1. Evals: how a PM knows the AI is right8 min
  2. Lab: build an eval set and a metrics plan25 min
06Prioritisation, roadmaps and stakeholders2 lessonsChoose what to build next with RICE, defend it with evidence, and communicate it so people act on it.
  1. RICE and the art of saying no8 min
  2. Lab: from 300 tickets to a RICE roadmap25 min
04Live projects

Two briefs. Real-shaped work.

  1. Project 1

    PRD and clickable prototype for a profit-leak alert

    Saarthi wants to alert marketplace sellers when money leaks out of their business through fee overcharges, weight-slab disputes, missing reimbursements, RTO spikes or wasted ad spend. Using the anonymised seller data pack, write the PRD for v1 of this alert, build a clickable prototype, and write the eval plan that would decide whether the AI explanations are good enough to ship.

    You hand in

    • PRD (max 5 pages): problem with evidence, scenarios, goals and non-goals, Given/When/Then acceptance criteria, AI behaviour spec, privacy section and Decisions log
    • Clickable prototype link (Lovable, v0 or Figma) showing at least three alert types and an evidence view, labelled 'example data'
    • Usability test log from at least two task-based sessions and the changes made
    • Eval plan: 40-case golden set with edge cases, LLM-judge rubric and calibration table, launch thresholds and rollback rule
    • A 5-minute recorded walkthrough defending the top three product decisions

    Data: Saarthi anonymised seller data pack (frozen version): settlement, MTR, returns, ads search-term and business reports for fictionalised sellers, plus 6 anonymised interview transcripts and a synthetic analytics event file.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

  2. Project 2

    From seller feedback to a RICE-scored roadmap

    You have 300 anonymised support tickets and 200 anonymised marketplace reviews from sellers of a fictional seller-tools product. Cluster them into themes you can defend, turn the top themes into opportunities, score them with RICE using sourced inputs, and present a one-quarter roadmap to a sceptical head of product.

    You hand in

    • Theme codebook with definitions, inclusion and exclusion rules, and the hand-checked label error rate
    • Opportunity list: two or three opportunities for each of the top five themes
    • RICE sheet with formula-driven scores, a source note on every input, and a sensitivity check
    • Now/Next/Later roadmap with a 'Not doing, and why' section
    • A 150-word stakeholder update and a 5-minute live defence with Saarthi playing the head of product

    Data: 300 anonymised support tickets and 200 anonymised reviews (identifiers replaced with codes) from the Saarthi sandbox, plus the anonymised seller data pack for reach estimates.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

05Practise out loud

In voice mock interviews and client calls, Saarthi plays: Meera, Head of Product at a fictional Bengaluru startup that builds tools for marketplace sellers, runs a 30-minute APM interview. She asks you to improve a feature for sellers, then pushes on how you would know an AI-generated alert is right, what you would cut, and which metric would make you roll it back. She interrupts vague answers with 'give me a number' and 'who exactly is the user?'

On plans that include mock interviews. You get written feedback after every call.

06Enrol

Pick a plan for AI Product Management.

One payment. The access deadline for each plan is shown before you pay.

  • Foundation

    01

    Learn the AI your field now runs on.

    ₹2,999

    One payment. GST included.

    • Access for 120 days (about 4 months) from the day you pay
    • No 1:1 mentor sessions
    • The common AI core and your domain course
    • Saarthi, your AI coach, any hour
    • Hands-on labs on free tools
    • Verifiable certificate
    Enrol on Foundation

  • Professional

    02

    Prove it on real work.

    ₹7,999

    One payment. GST included.

    • Access for 180 days (about 6 months) from the day you pay
    • One 1:1 session with a mentor
    • Everything in Foundation
    • Two live projects, reviewed by Saarthi and a mentor
    • Public portfolio page of verified work
    • Voice mock interviews
    • One 1:1 coaching session
    Enrol on Professional

  • Career

    03

    Work alongside our team.

    ₹14,999

    One payment. GST included.

    • Access for 240 days (about 8 months) from the day you pay
    • Four 1:1 sessions with a mentor
    • One 1:1 call with an industry expert
    • Everything in Professional
    • Four-week supervised practicum with the Saarthi team
    • Four 1:1 coaching sessions with your mentor
    • An exclusive 1:1 call with an industry expert in your field
    • CV and LinkedIn review
    • Priority mentor replies
    Enrol on Career

Your access period starts the day you pay and is shown before you pay. One exam pause per enrolment, for up to 15 days. When access ends the course locks, and your portfolio page and certificate stay live. Full refund within 7 days, pro-rata after that, and an automatic refund for any practicum, 1:1 mentor or expert session we do not deliver. Refund policy