Become a Job Ready
AI First Product Manager
in 12 Weeks
Alumni now ship AI at Google, Microsoft and Oracle. Saturdays go deep on RAG, agents, evals, LLM systems and model tradeoffs. Sundays are interview prep and hands-on building.
You don't finish with just a certificate. You leave with a working product, an eval framework, and the answers for the interview that follows.
Week-by-week breakdown
Saturdays + Sundays · 4 sessions/week (10:30 AM–12:30 PM & 2:30–4:30 PM IST) · 8 hrs/week · Weeks 1–10
What our students are saying
Real feedback from students who've learned with Shailesh across courses, YouTube, mentorship, and 1:1 coaching.
"So far it has been really good. I just completed a program at ISB before yours started. If I compare the two, the concepts here are much more hands-on. It is not just limited to theory. The recommendations, the references, the infrastructure- I feel a lot of things are better here. It is genuinely worth the investment. And the concepts — I will not say they are entirely new because some of them we learned in our master's or degree programs. But when you explain them, it is much easier to relate them to what we actually do day in and day out. That really helped me. Yesterday's class especially — I understood everything when you explained it."
The cohort pushed me to ship, not just plan. I built the Culture Welcome Portal for a paying client (4-figure monthly USD), a culture-first employee experience platform any company can brand and deploy in under an hour. It changed how I think about building products.
After the cohort, I feel extremely confident building AI products. Earlier I was only using Google AI Studio; now I'm comfortable with Claude Code. I built a RAG application from scratch, and at work I built a learning platform for newcomers on my team. I'm confident debugging too, because I now understand the components of an AI product: prompts, context management, guardrails, continuous evaluation. Building my own applications and working hands-on with those components is what made the difference.
I just landed a Senior Product Manager role in AI and data governance. I used so many learnings from the AI PM Cohort in my interview rounds — at one point, I could literally see my interviewers' faces, shocked at how much I knew about AI. All thanks to Shailesh and Apoorva; the cohort learnings carried me through every round.
I got an offer at eGov Foundation. I'll be working under the CTO of Aadhaar. The RAG session in the cohort was a game-changer. In my interviews, I was questioned about a product I hadn't directly owned, but the hands-on RAG work gave me the confidence to speak about it as I built it. I'm super invested — the learnings don't stop here, because I'll be owning AI products in my new role, and everything I learned in the cohort directly applies.
After the cohort, I'm building with Claude Design and vector DBs, and I understand AI processes well enough now to spot the errors. This cohort has helped extensively with AI Interview Preparation as well.
Over the course of this cohort, I've built a much stronger understanding of how to create AI-native products end to end. I can identify where AI creates value, frame the opportunity, choose the right architecture.I'm comfortable with prompt engineering, RAG fundamentals, prompt chaining, evaluation, debugging hallucinations, prompt injection defence, and AI product design frameworks — and I can rapidly prototype and ship using ChatGPT, Claude, Gemini, Cursor, GitHub Copilot, Colab, Vercel, Supabase and Next.js. One of my biggest takeaways has been a structured approach to debugging. Instead of assuming the model is the problem, I isolate the issue across prompts, context, retrieval quality, embeddings, the RAG pipeline, model selection, latency, evaluation metrics, guardrails and business logic.
The cohort is really helpful. It teaches you a lot about the AI/ML field from a Product Management perspective. I highly recommend it. The best part is it is hands-on and not just theory.
I recently cleared the interview for a role at Rentickle. One of the questions was to design an AI furniture rental assistant for them — which was exactly what you covered in the Week 2 session. It really helped me in the interview. Thank you, Sir!
The program has increased my confidence in building products with AI, I'm using Claude and Perplexity now. Through the course, the high-level architecture of AI products became clear to me, including how the layers are designed to improve the cost and latency of AI features.
I'm definitely more confident after the AI PM Cohort. I built PMPrep AI — an AI PM interview coach that gives instant feedback — and it's given me a lot of clarity on how to structure AI products, especially around scoring and evaluation. I'm working with Claude Code, Next.js, React and Vercel. Debugging is where the real shift happened: I test a lot, check whether the feedback actually makes sense, tweak prompts when they miss, and make sure the scoring rubric is doing what I expect.
Built for builders who want to ship real AI
Not for passive learners. This is a doing cohort — you ship something real by Week 12.
PMs transitioning into AI PM roles — building hands-on credibility with working prototypes, not just theory
Mid-to-senior PMs (3–10 years) who want to lead AI initiatives or move to AI-first companies
Engineers, data scientists, and designers pivoting into AI Product Management roles
PMs preparing for AI PM interviews — product sense, metrics, strategy, behavioural, and technical AI questions
Builders who want a structured path through AI fundamentals, RAG, AI agents, evals, and GTM — with a capstone
Anyone who has watched scattered AI tutorials and wants a single, sequenced, mentor-led program
The course teaches you AI PM.
The cohort makes you one.
The self-paced course gives you knowledge. The cohort gives you more knowledge, proof of work, interview readiness, and mentor access — the things that actually get you hired.
| Self-Paced Course | 12-Week Live Cohort Starts Soon | |
|---|---|---|
| Format | 45 recorded videos, ~7 hrs total | 45 live sessions, ~90 hrs total |
| Pace | Self-paced, lifetime access | Structured 12-week schedule |
| Projects you build | 1 prototype demo | 10+ projects across 12 weeks |
| Capstone product | Full AI product: spec → prototype → evals + Demo Day | |
| Hands-on Build Hours | 10 dedicated sessions — RAG, Agents, Evals, UX Audit, Prototyping | |
| Mentor feedback | Weekly office hours + scored reviews | |
| Interview prep | 20 questions + answers (recorded) | 10 dedicated sessions (20 hrs) + frameworks to tackle every AI PM question type |
| Mock interviews | 2 full 3-round loops, mentor scored + written feedback | |
| Demo Day | Live 8-min presentation + panel Q&A + Best Project awards | |
| Strategy & depth classes | GTM, AI Pricing, Model Selection, Latency, AI Safety, AI Analytics, AI ROI | |
| SDD + AI Agents + UX | Spec-Driven Development, Agents deep dive (MCP, A2A), UI/UX for AI | |
| AI Case Studies (B2C + B2B) | PDF book only | 4 hrs of live case study discussion with frameworks |
| Tools: n8n, Claude Skills, Lovable | Lovable demo only | Hands-on with all three + deployment |
| Peer community | Alumni network + job board | |
| Best for | Learning AI PM concepts at your own speed | Getting job-ready with proof of work + portfolio |
The course gives you knowledge. The cohort gives you knowledge + proof of work + interview readiness + mentor access.
Week-by-week deliverable map
Every learner builds toward the same outcome — a portfolio-ready AI product with full spec, prototype, evals, GTM, and live demo.
| WK | MILESTONE | WHAT YOU DELIVER | FORMAT |
|---|---|---|---|
| 01 | Problem Definition | AI Opportunity Canvas — problem, user segment, solution gaps, AI angle | Notion doc / 1-pager |
| 02 | Technical Foundation | ML Pipeline Map — algorithm selected, data pipeline, model card | Diagram + rationale |
| 03 | RAG Prototype | RAG design + working prototype: architecture, knowledge base, retrieval strategy | Architecture doc + prototype |
| 04 | Agent Prototype | Agent design + working prototype: autonomy level, tools, memory, HITL, tested on 3 scenarios | Architecture doc + prototype |
| 05 | Eval Framework | Eval spec + 10-case golden set + launch threshold + evals run on prototype | Eval spec + test results |
| 06 | Full Spec + Prototype | Complete spec + polished prototype, mini case study started | Spec doc + prototype link |
| 07 | UX Audit + Polish | 8-principle UX audit, HITL pattern, AI UX copy, prototype finalised, case study complete | UX audit + prototype |
| 08 | Quality & Metrics | Risk audit + responsible AI section + NSM/supporting/guardrail metrics + A/B test plan | Risk doc + metrics |
| 09 | Strategy + Model Decision | GTM one-pager + model comparison matrix + cost + latency strategy | Strategy doc + model doc |
| 10 | Interview Ready | 15-answer story bank, 2 mock rounds scored, enterprise case study teardown | Interview doc + case analysis |
| 11 | Presentation Ready | Full capstone deck (10–12 slides) + demo video + mock demo + 2 interview answers | Deck + demo video |
| 12 | 🎤 Demo Day | Live 8-min presentation + portfolio published + LinkedIn post + certificate awarded | Live demo + portfolio |
Every single thing you get
Not a recorded course. Every item below is live, hands-on, and mentor-guided.
What you walk away with
Every output below is built, not watched. You'll have a portfolio to show on Demo Day.
A portfolio-ready AI capstone: problem → spec → prototype → evals → GTM → Demo Day
AI fundamentals mastered — supervised/unsupervised learning, deep learning, LLMs, CoT + ToT prompting
Working RAG system and AI agent prototype with documented architecture and HITL checkpoints
Production-grade evals — golden test sets, RAGAS, LLM-as-judge, launch threshold defined
AI PM strategy depth — model selection, latency, cost-quality tradeoffs, GTM, competitive moats
8 structured Interview Prep modules + 2 full mock rounds with mentor scoring and written feedback
15-answer STAR story bank, polished interview prep doc, capstone framed as interview answers
Live Demo Day — panel Q&A, certificate awarded, portfolio published, LinkedIn launch post
Your questions, answered
Ready to Build Your AI Product?
12 weeks. 45 live sessions. Working prototypes. Mock interviews. Live Demo Day. Rolling applications.