Who this is for
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
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What our students are saying
Real feedback from students who've learned with Shailesh across courses, YouTube, mentorship, and 1:1 coaching.
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Video Testimonials
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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 + accountability partner |
| 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.
90+
Hours Live
10+
Projects Built
20
Hrs Interview Prep
2
Mock Interview Rounds
1
Portfolio-Ready Capstone
12-Week Curriculum
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
Phase 1 · Weeks 1–3
AI Foundations
Week 1
AI Fundamentals & Algorithms
Lock your capstone problem statement
Build AI literacy from first principles. Understand what makes a product 'AI-powered' and lock in your capstone problem statement.
🔵 Sat AM — Concept: AI Fundamentals
- Supervised vs Unsupervised Learning — how models learn from labelled vs unlabelled data
- What makes a product 'AI-powered' vs just data-driven
- The AI Flywheel: how user data compounds model quality and moat
- AI product taxonomy: predictive, generative, agentic
🔵 Sat PM — Concept: Algorithms & Use Cases
- Logistic Regression, Clustering, Decision Trees, SVM, Random Forest, XGBoost
- Match each algorithm to a real PM use case
- Identify the ML technique for your capstone + write algorithm selection rationale
📝 Sun AM — Interview Prep 1: First Principles
- CIRCLES and STAR frameworks for AI PM interviews
- Clarifying questions: user, metric, constraint, timeline, model constraints
- Build your personal clarifying question bank (10 Qs)
🟣 Sun PM — Office Hours
- Q&A on Week 1 concepts + assignment review
- Review AI Opportunity Canvas submissions
Capstone Milestone: AI Opportunity Canvas + clarifying question bank (10 Qs) delivered.
Week 2
Generative AI & ML Systems
LLMs, transformers, production pipelines
Build a real mental model of how LLMs and ML production systems actually work — and apply it to your capstone.
🔵 Sat AM — Concept: Generative AI Deep Dive
- Deep Learning: neurons, backpropagation, transformer architecture
- LLMs: tokenisation, attention, pre-training vs fine-tuning vs RLHF
- Advanced Prompting: CoT, ToT, few-shot, system prompts
🔵 Sat PM — Concept: ML Systems & Pipelines
- Training vs inference pipelines, batch vs streaming data
- Feature stores, model registries, experiment tracking
- Monitoring: data drift, model degradation, latency vs accuracy tradeoffs
📝 Sun AM — Interview Prep 2: AI Product Sense
- Evaluating AI features: accuracy, latency, trust, explainability
- Improvement framework: current state → pain points → prioritised solutions
- Answer 2 product sense questions live + write structured improvement pitch
🟣 Sun PM — Office Hours
- GenAI Q&A + pipeline review + prompt engineering practice
- Peer review of pipeline maps
Capstone Milestone: GenAI feature defined. ML pipeline map + 2 product sense answers delivered.
Week 3
GenAI Tools, AI PM Role & RAG
Ship your first working RAG prototype
Understand the modern AI stack and ship your first working RAG prototype.
🔵 Sat AM — Concept: GenAI Tools & AI PM Role
- GenAI tool landscape: ChatGPT, Claude, Gemini, Cursor, Midjourney
- AI product stack layers: infra → model → application → UX
- What AI PMs actually do in 2026: scope, skills, cross-functional dynamics
🔵 Sat PM — Concept: RAG Deep Dive
- RAG architecture: retriever + generator + knowledge base
- Vector DBs, embeddings, chunking strategies, retrieval tuning
- RAG vs fine-tuning — PM decision framework
📝 Sun AM — Interview Prep 3: Metrics RCA & Guesstimate
- RCA for AI: segmentation by cohort, device, model version
- Guesstimate frameworks: market sizing, cost estimation
- Solve 2 RCA + 2 guesstimate questions live
🟢 Sun PM — Demo Hours: Build RAG Prototype
- Build a simple RAG prototype using no-code tools
- Connect knowledge base, configure retrieval, test 5 queries
- Peer pairing: test each other's prototypes
Capstone Milestone: AI stack diagram + RAG prototype + 3 RCA / guesstimate answers.
Phase 2 · Weeks 4–7
Building AI Products
Week 4
AI Agents: Concept + Hands-on Build
Design and prototype your AI agent same day
Design and prototype an AI agent — autonomy levels, tool use, memory, HITL — same weekend as the concept.
🔵 Sat AM — Concept: AI Agents Deep Dive
- Autonomy levels L1–L4, tool use, memory types (short/long/episodic)
- Planning loops: ReAct, chain-of-agents, reflection patterns
- Multi-agent systems: orchestrator-worker, MCP, A2A
- Real examples: Claude computer use, Copilot agent mode, Devin
🟢 Sat PM — Demo: AI Agents Hands-on Build
- Design the agentic workflow for your capstone
- Prototype using Claude, LangChain, or no-code tools
- Test on 3 scenarios: happy path, edge case, failure
📝 Sun AM — Interview Prep 4: Metrics NSM & Execution
- North Star metric + counter-metrics + guardrail metrics
- 'DAU dropped 20% — walk me through it' execution questions
- OKR setting for AI product teams
🟢 Sun PM — Demo Hours: Agent Prototyping Continued
- Refine agent based on Sat test results
- Continue MCP & A2A prototyping
- Peer review + mentor feedback on architecture decisions
Capstone Milestone: Agent architecture designed + prototype built + NSM + execution answers banked.
Week 5
Advanced Evals: Spec → Test → Launch Threshold
Write production evals, define "good enough to ship"
Write production evals, build a golden test set, and decide what 'good enough to ship' means for your product.
🔵 Sat AM — Concept: Advanced Evals
- Eval types: automated metrics, human evaluation, LLM-as-judge
- BLEU, ROUGE, faithfulness, groundedness, RAGAS for RAG
- Eval pipeline tools: LangSmith, Braintrust, PromptFoo
🟢 Sat PM — Demo: Evals Hands-on Implementation
- Write the eval spec for your capstone AI feature
- Create 10 golden test cases (happy path + edge cases)
- Run manual evals, score results, set launch threshold
📝 Sun AM — Interview Prep 5: AI Strategy, Growth & Pricing
- Growth: PLG vs sales-led, AI data network effects
- Pricing: token-based, usage-based, outcome-based, flat subscription
- 'How would you grow Perplexity by 10x?' — answer live
🟣 Sun PM — Office Hours: Eval Spec Review
- Review eval specs and golden test sets
- Q&A on eval metrics + troubleshoot scoring issues
Capstone Milestone: Eval framework written + golden test set (10 cases) + launch threshold defined + growth & pricing answers banked.
Week 6
Spec-Driven Dev & Live Build
Spec → prototype → test → ship in one weekend
Master spec-driven development. Use Claude/AI tools to go from spec to working prototype — in one weekend.
🔵 Sat AM — Concept: Spec-Driven Dev & Claude Skills
- Anatomy of a great spec: context, user stories, constraints, examples, eval criteria
- Vibe coding: prototyping without full engineering support
- Spec-first AI development — the modern PRD-to-shipped handoff
🟢 Sat PM — Demo: Spec → Prototype Live Build
- Write full spec → generate prototype using Claude/AI tools
- Iterate: spec → build → test → revise → rebuild
- Document build process as mini case study
📝 Sun AM — Interview Prep 6: GTM & Market Entry
- 'How would you launch a new AI coding assistant?' — end to end
- Structuring GTM: market sizing → beachhead → channels → metrics
- AI-specific GTM: trust, explainability, enterprise procurement
🟢 Sun PM — Demo Hours: Lovable Prototyping
- Continue building on Lovable
- Run eval golden set against prototype
- Mentor office hours for stuck projects
Capstone Milestone: Full spec written + working prototype built + tested against evals + GTM answers banked.
Week 7
AI Product Design & UX Polish
Trust signals, HITL, AI-native UX patterns
Design for non-determinism. Polish your prototype with trust signals, HITL, and AI-native UX patterns.
🔵 Sat AM — Concept: AI Product Design & UX
- Designing for non-determinism, trust calibration, streaming + skeleton screens
- HITL patterns: approval flows, correction loops, escalation
- Explainability UX, confidence indicators, anti-patterns
🟢 Sat PM — Demo: UX Audit & Prototype Polish
- 8-principle UX audit of your capstone feature
- Redesign error + loading states; write AI UX copy
- Peer UX review, polish prototype based on audit findings
📝 Sun AM — Interview Prep 8: Behavioural Interview
- STAR for AI PM contexts: shipping under uncertainty, data science disagreements
- 'Why AI PM?' — authentic answers
- Start building your 5-story bank
🟢 Sun PM — Demo Hours: N8N Agent Building
- Hands-on N8N agent building session
- Final prototype refinements + mini case study completion
Capstone Milestone: UX audit complete + prototype polished + mini case study finalised.
Phase 3 · Weeks 8–10
Strategy & Depth
Week 8
AI Risks, Biases & Product Metrics
Responsible AI + the full metrics stack
Add the responsible-AI and analytics layer your capstone needs to ship credibly.
🔵 Sat AM — Concept: AI Risks & Biases
- Bias types: data, representation, algorithmic, output
- Hallucinations: why LLMs confabulate + PM mitigation strategies
- Regulatory landscape 2026: EU AI Act, India DPDP Act
🔵 Sat PM — Concept: Product Metrics & AI Analytics
- AI operational metrics: accuracy, token cost, latency, uptime
- A/B testing AI features: non-determinism + evaluation lag challenges
- Using AI to analyse metrics: SQL + LLM combos
📝 Sun AM — Interview Prep 8: AI General Questions
- RAG vs fine-tuning, transformers explained, LLM production risks
- STAR bank: 5 stories + 5 AI general knowledge Qs answered
- Record 'Why AI PM?' and review
🟣 Sun PM — Office Hours: Risk + Metrics Q&A
- Review risk audits + metrics frameworks
- Peer feedback on dashboards + STAR answer practice
Capstone Milestone: Risk audit + responsible AI section + full metrics stack (NSM, guardrails, A/B test plan) + STAR bank (5 stories).
Week 9
GTM, Model Selection & Mock Interview Round 1
Moats, model decisions, go-to-market + first full mock loop
Lock GTM strategy, make your model selection decision, and complete your first full 3-round mock interview loop.
🔵 Sat AM — Concept: GTM & Market Entry
- PLG vs sales-led vs community-led for B2B AI tools
- Competitive moats: data, distribution, UX, switching cost, speed
- Launch playbook: beta → early access → GA
🔵 Sat PM — Concept: Model Selection, Latency & Tradeoffs
- Model comparison: accuracy, latency, cost/token, context window
- Cost-quality tradeoffs: GPT-4o vs Haiku vs open-source
- Build vs buy vs fine-tune decision matrix
📝 Sun AM — Interview Prep 7: Evals, Model Selection & Tradeoffs
- 'How would you evaluate a RAG customer support bot?'
- 'Our AI feature takes 8 seconds — what do you do?'
- Framework: constraints → options → criteria → recommendation
📝 Sun PM — Mock Interview Round 1
- Full 3-round loop (45–60 min): Product Sense + Metrics + Strategy
- Mentor as interviewer with real-time scoring
- Structured feedback + top 2 improvement areas per round
Capstone Milestone: GTM one-pager + model selection matrix + cost estimates + latency strategy. Mock Round 1 scores recorded.
Week 10
Enterprise Case Studies & Mock Interview Round 2
Pattern recognition + final interview mastery
Analyse real enterprise AI launches, complete both mock rounds, and lock your 15-answer story bank — job-ready before Demo Day.
🔵 Sat AM — Concept: Enterprise AI Case Studies (B2C)
- Consumer AI product: idea → scale — full post-mortem
- Apply Weeks 1–9 frameworks to analyse each case
🔵 Sat PM — Concept: Enterprise AI Case Studies (B2B)
- Enterprise AI deployment: procurement, pilot, rollout
- Common success patterns + post-mortems of AI product failures
📝 Sun AM — Mock Interview Round 2
- Full 3-round loop: Behavioural + Technical AI + Case Study
- Stricter scoring than Round 1, peer interviewer practice
- Compare Round 1 vs Round 2 scores + final readiness assessment
🟣 Sun PM — Office Hours: Interview Debrief + Story Bank
- Group debrief: common mock interview mistakes
- Rewrite weakest 3 answers + finalise 15-answer story bank
- Practice elevator pitch for capstone
Capstone Milestone: Case study teardown + interview prep doc + 15-answer story bank finalised. Both mock rounds complete.
Phase 4 · Weeks 11–12
Demo Prep & Demo Day
Week 11
Capstone Refinement & Mock Demo
Turn your capstone into a tight 8-minute story
Turn your capstone into a tight 8-minute story. Rehearse until it's Demo-Day ready.
🔵 Sat AM — Concept: Capstone Refinement
- Capstone structure: Problem → User → AI Solution → Stack → Evals → Metrics → Risks → GTM
- Handling 'why not just use ChatGPT?' — peer review with scoring rubric
🎤 Sat PM — Mock Demo Presentations
- Live mock demo: 5 min each + mentor feedback + panel Q&A practice
- Peer feedback on 2 projects + tighten narrative
🎤 Sun AM — Final Polish + Rehearsal
- Full timed run-through + interview framing of capstone
- Write 2 interview answers using capstone as story
- Last-minute troubleshooting + confidence building
Capstone Milestone: Full deck (10–12 slides) + demo video recorded + mock demo delivered. Final version ready for Demo Day.
Week 12
🎤 Demo Day
Live presentation · Portfolio published · Certificate awarded
Ship publicly. Present your AI product to a panel. Walk away job-ready with portfolio + certificate.
🎤 Demo Day Session 1
- 8-min capstone presentation + 5-min Q&A from mentor + peer judge panel
- Scoring: problem clarity, solution depth, technical credibility, metrics
🎤 Demo Day Session 2
- Remaining presentations + Best Project awards
- Speed interview round: 2 Qs per learner in front of cohort
Wrap-up & Next Steps
- Portfolio published + LinkedIn launch post
- Certificate awarded + alumni community access
- Async course + job board + accountability partner assigned
✅ COURSE COMPLETE. Portfolio-ready capstone delivered. Interview answers polished. AI PM job-ready.
Capstone Milestones
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 |
Everything Included
Every single thing you get
Not a recorded course. Every item below is live, hands-on, and mentor-guided.
45 live sessions across 12 weeks
10 hands-on Demo / Demo Hours sessions
8 structured Interview Prep modules
2 full Mock Interview rounds (mentor scored)
Office Hours throughout for Q&A + STAR practice
Portfolio-ready AI product capstone (all 12 weeks)
Working RAG prototype + AI Agent prototype
Eval framework with golden test set + launch threshold
Spec-driven dev workflow using Claude / AI tools
8-principle UX audit + AI UX copy
Bias audit + Responsible AI section + 3 launch guardrails
Full metrics stack: NSM + supporting + guardrails + A/B
GTM one-pager + model selection matrix + cost strategy
15-answer STAR story bank across 2 mock rounds
Final capstone deck (10–12 slides) + recorded demo video
🎤 Live Demo Day + Best Project awards + speed interviews
Certificate of completion
Async course access + alumni community + job board
All session recordings + bonus content vault
Accountability partner for post-cohort job search
Core Outcomes
What you walk away with
Every output below is built, not watched. You'll have a portfolio to show on Demo Day.
01
A portfolio-ready AI capstone: problem → spec → prototype → evals → GTM → Demo Day
02
AI fundamentals mastered — supervised/unsupervised learning, deep learning, LLMs, CoT + ToT prompting
03
Working RAG system and AI agent prototype with documented architecture and HITL checkpoints
04
Production-grade evals — golden test sets, RAGAS, LLM-as-judge, launch threshold defined
05
AI PM strategy depth — model selection, latency, cost-quality tradeoffs, GTM, competitive moats
06
8 structured Interview Prep modules + 2 full mock rounds with mentor scoring and written feedback
07
15-answer STAR story bank, polished interview prep doc, capstone framed as interview answers
08
Live Demo Day — panel Q&A, certificate awarded, portfolio published, LinkedIn launch post
Hands-On Stack
Tools You'll Actually Build With
Not slides. Not demos. You get hands-on with every tool in live sessions — the same stack used by AI PMs and engineers at top companies in 2026.
🧠 AI & LLM Layer
🤖
Claude (Anthropic)
Prompting, spec-driven dev, agentic builds
🎨
Claude Code & Claude Design
AI-native coding + design prototyping
🔧
Claude Skills
Reusable skill libraries for AI workflows
🌐
Google AI Studio
Gemini models, multimodal AI experiments
🤗
Hugging Face
Open-source models, embeddings, fine-tuning
⚙️ Build & Orchestration
🔗
LangChain
LLM pipelines, chains, agent tools
🕸️
LangGraph
Stateful multi-agent graphs & loops
⚡
N8N
Visual agent automation & workflow deployment
💜
Lovable
Vibe-coding: spec → working product fast
🐍
Python + Google Colab
Scripting, data wrangling, model experiments
🎯 Why This Stack?
These aren't picked randomly. Every tool in this cohort maps directly to a real AI PM or engineer workflow you'll encounter in your next role or interview.
All tools used in live Demo sessions — not just slides
You deploy working prototypes using these exact tools
These tools appear in AI PM & engineer job descriptions daily
Interviewers ask about LangChain, agents, and RAG in rounds
Career Acceleration
Referrals to Top Companies
Shailesh personally mentors each cohort member through the assessment process. For candidates who demonstrate strong preparation and portfolio work, he opens direct referral pathways at companies he's connected with.
Important: Referrals are not guaranteed and are entirely at Shailesh's discretion based on individual assessment. This is a merit-based pathway — not a placement promise. The cohort is a skills and portfolio program first.
Frequently Asked
Your questions, answered
Do I need to be a developer or engineer to join?
No. The cohort is designed for PMs, engineers, designers, and data scientists alike. All prototyping is done using no-code and AI tools (Claude, LangChain, no-code RAG builders). You need to be able to think through product decisions and use AI tools to build — not write production code.
How much time per week do I need to commit?
8 hours per week for Weeks 1–10 (4 live sessions of 2 hrs each on Saturday + Sunday). 6 hours per week for Weeks 11–12 (Demo Prep and Demo Day). All sessions run on Saturday and Sunday mornings and afternoons IST — designed not to conflict with weekday work.
What if I miss a session?
All sessions are recorded and shared. Office Hours run throughout the cohort so you can catch up. We strongly recommend attending live — the real value is in Q&A, peer reviews of your capstone work, and real-time mentor feedback on your specific prototype.
What does the capstone look like at the end?
A complete portfolio-ready AI product: a written spec, working prototype (RAG or agent), eval framework with a golden test set and launch threshold, GTM one-pager, risk + metrics doc, a polished 10–12 slide deck, and a 3-min recorded demo video. You present it live on Demo Day in front of a panel.
Is this heavy on theory or hands-on building?
Heavily hands-on. Every concept week is paired with a Demo/Demo Hours session where you build something the same week. By the end you will have built a working RAG prototype, a working AI agent, run your own evals, and shipped a full spec-driven prototype — all before Demo Day.
How does the application process work?
Fill in the application form. The cohort is capped — applications are reviewed on a rolling basis. If you're a fit, you'll receive the payment link. Seats are allocated in order of approved application + payment.
Ready to Build Your AI Product?
12 weeks. 45 live sessions. Working prototypes. Mock interviews. Live Demo Day. Rolling applications.