Live cohort 3 In association with AtliQ Technologies

Transition into an AI product manager role: Discover, build and ship an AI product in 10 weeks

Ten weeks, live, for working professionals. You will not collect frameworks. You will find a real AI opportunity, write the AI PRD, design the AI UX, vibe code a RAG chatbot and an agent, put numbers on cost, design evals and guardrails and build them into your AI products, and pitch your capstone to mentors playing your stakeholders.

Enroll now → See the 10 weeks

Cohort 3 begins Saturday, 24^th October. 20 live sessions, Saturdays and Sundays, 5:00 to 8:00 PM IST.

Inner circle price

US$630

Closes Tuesday, 20^th October. After that the price is US$840.

Includes the Soft Skills Bootcamp · Already own it? You pay less · Enrollment closes Saturday, 31^st October.

10 weeks

Live, Saturdays and Sundays

From 24^th October

20 live sessions

Three hours each, 5:00 to 8:00 PM IST

Recorded, with 1 year of access

Office hours

45 minutes, every alternate week

Doubts cleared between weekends

1 capstone

Your AI product, start to finish

Top 3 presented on Demo Day

1.5M+

YouTube Subscribers

739K+

Learners

85K+

Paid learners

7000+

5 Star Reviews

The 10 weeks

Two versions of you exist ten weeks from now.

Scroll. The line is what changes when you spend ten weeks building AI products instead of reading about them.

What you can ownWk 1

1 Week 1

Know what AI can and cannot do.

The AI PM role and why it exists now, the AI family tree, machine learning, generative AI, and the ten jargons you will hear in every meeting.

Ends with: an AI landscape and role analysis

2 Week 2

Discover real problems and manage stakeholders.

Why AI projects fail, the AI discovery framework, user research (the Mom Test, 5 Whys, empathy mapping), and mastering stakeholder management.

Ends with: an AI discovery framework & stakeholder map

3 Week 3

Prompting, context, and vibe coding.

Master the RCTF prompting framework, understand embeddings and vector databases, and build a working RAG chatbot live in class.

Ends with: a live working RAG chatbot prototype

4 Week 4

AI agents and unit economics.

Understand the ReAct loop to build multi-agent systems, and put numbers on AI by calculating token costs, inference costs, and ROI against human support.

Ends with: a multi-agent system & ROI financial model

5 Week 5

AI evals, metrics, and guardrails.

Define what "good enough to ship" means using human evaluation and LLM-as-a-judge, read precision/recall metrics, and design guardrails to mitigate hallucinations.

Ends with: an LLM evaluation framework & guardrail suite

6 Week 6

Opportunity mapping, metrics, and capstone launch.

Master product & business metrics (North Star, ROI, retention, cost-to-serve), understand model & context drift, map AI opportunity workflows using Miro, and officially kick off your Capstone project.

Ends with: product metrics mapping & Capstone project launch

7 Week 7

Production AI PRDs and AI UX guidelines.

Learn how AI PRDs differ from traditional ones to write a production AI PRD, and explore Microsoft's HAX Toolkit to design for trust, transparency, and explainability.

Ends with: a production AI PRD & HAX UX guidelines

8 Week 8

Strategy, compliance, and feasibility.

Navigate the EU AI Act for your go-to-market strategy, and run a live feasibility and business impact analysis with an actual client.

Ends with: a live client feasibility & compliance analysis

9 Week 9

Data strategy and AI system design.

Assess data readiness, understand production trade-offs (cost vs quality vs latency), and design AI systems using real-life case studies from AtliQ's AI team.

Ends with: a production AI system architecture design

10 Week 10

Career readiness and Demo Day.

Get essential interview and portfolio prep, including a live mock interview session with a volunteer, and present your Capstone strategy (or Build Your Own Product) to an expert panel on Demo Day.

Ends with: a Capstone presentation to expert panel on Demo Day

What you leave with

Build your AI PM portfolio with 8 practical artifacts and one capstone.

Turn learning into tangible work, from user research and working AI prototypes to a complete product strategy. Showcase how you think, build, and make decisions as an AI Product Manager.

Build 1

User research toolkit

Create an Empathy Map, User Persona, and 5 Whys Analysis to uncover user needs and define the right problem.

Build 2

RAG chatbot

Use vibe coding to build a chatbot that answers questions using your own documents and knowledge sources.

Build 3

AI agent

Build an AI agent to carry out tasks and automate a workflow, turning your learning into a working prototype.

Build 4

RAG cost estimation sheet

Estimate the costs of running a RAG application and connect them to pricing and unit economics.

Build 5

AI opportunity map

Create a Miro board to map AI opportunities and prioritize use cases worth pursuing.

Build 6

AI product requirements document

Turn an AI idea into a structured PRD using an industry-standard template to guide product development.

Build 7

AI UX analysis

Apply the HAX Toolkit to popular platforms, evaluate their AI experiences, and identify opportunities for improvement.

Build 8

Feasibility and impact assessment

Assess a real client problem to evaluate feasibility, business impact, and whether an AI solution is worth pursuing.

Capstone

An AI product you defend on Demo Day

Write a PRD, design architecture, define evals and guardrails, and present a complete AI product strategy. Mentors act as your stakeholders to mirror real-world AI PM interactions.

The stack

The tools AI product teams actually use in 2026.

Claude PRD drafting and Claude Code

ChatGPT LLM comparison

LangChain Agent and RAG framework

Groq Fast LLM inference

Streamlit Chatbot UI

VS Code Build environment

Vercel App deployment

Lovable AI builder

Miro Opportunity mapping

HAX Toolkit AI UX guidelines

Mixpanel Product analytics

Excel Cost modelling

Real business exposure

You are not writing a strategy deck. You are working on real business problems.

In association with AtliQ Technologies, which has served 380+ businesses and delivered 140+ software solutions across 7+ countries.

1 to 3

simulated client meetings where mentors act as stakeholders for your capstone project.

Live feasibility and impact analysis with an actual AtliQ client.

AI projects you build through vibe coding: a RAG chatbot and an AI agent

380+

businesses AtliQ has served, the source of your case studies

10 weeks. 20 live sessions. From fundamentals to Demo Day.

Saturdays and Sundays, 5:00 to 8:00 PM IST. Open any week to see every session and what it covers.

AI PM Fundamentals & AI Foundations

Session 1 - The AI Product Manager

The AI PM role and why it exists now • Traditional PM vs AI PM

Session 2 - AI Foundations for Product Managers

The AI family tree • Generative AI and transformers • Analytics vs Automation vs ML vs Gen AI • The three eras of AI · ML vs traditional software • Supervised vs unsupervised learning • Neural networks and deep learning • Emergence of Generative AI • The 10 AI jargons

Stakeholder Management & AI Problem Discovery

Session 3 - Stakeholder Management + Personal Branding

Communication as a timeless PM skill • The AER Framework • Listening ratio & self-awareness • Stakeholder mapping & power-interest grid • How to build your personal brand on LinkedIn

Session 4 - AI Problem Discovery & User Research

Why AI projects fail • Perceived vs real problem • The AI PM Discovery Framework • Mom Test & JTBD interviews • Persona, empathy map & 5 Whys

Prompting, Context & Vibe Coding (RAG Application)

Session 5 - Prompting, Context & RAG for AI PMs

The RCTF prompting framework • Prompting techniques: zero-, one-, and few-shot • System prompts • Embeddings • Vector databases & semantic search • RAG: indexing and retrieval • What is RAG & why RAG (limitations of prompting alone) • How RAG works • Fine-tuning vs Prompting vs RAG • RAG for PMs: use cases, trade-offs & when to reach for it

Session 6 - Vibe Coding (Building a RAG Application)

Live RAG chatbot build • PRD-first approach • How RAG works • Tech stack: Groq, LangChain, Streamlit, an IDE, Claude Code/Cowork, and Git/GitHub • Claude Design

AI Agents, Cost Estimation & Unit Economics

Session 7 - AI Agents

What is an AI agent • The ReAct loop • Multi-agent systems • Agents vs workflows • Multimodal AI • Tool calling (MCP covered if possible) • Memory • LangChain vibe coding project activity

Session 8 - Cost Estimation for RAG & Gen AI Systems + Pricing & Unit Economics

Token fundamentals • OpenAI pricing models • Inference cost • Embedding cost • ROI vs human support cost • Pricing models • Unit economics • Hands-on cost estimation activity

AI Metrics, Evals Foundations & Guardrails

Session 9 - AI Metrics & AI Evals Foundations

AI metrics - what to measure and why • Product, Business & AI metrics - how they differ • AI Eval Stack - what “good enough to ship” means • Human Evaluation vs LLM-as-a-Judge • Hands-on activity: evaluating a sample AI output

Session 10 - AI Evals Part 2 & Guardrails

Precision, Recall & F1, AUC-ROC: reading metrics to make a trade-off call • What guardrails are & why PMs need them • Hallucination mitigation • Safety guardrails: defining failure modes to block

Product & Business Metrics + AI Opportunity Mapping (Capstone Launch)

Session 11 - Product & Business Metrics + Capstone Launch

Product metrics (engagement, retention, adoption) • Business metrics (revenue, ROI, cost-to-serve) • North Star Metric • Model drift, context drift & data drift • Micro-activity: metrics mapping exercise • Capstone project launch

Session 12 - AI Opportunity Mapping

AI opportunity mapping • The Miro process board • 33A AI cards • Workflow mapping for AI use cases • Guardrails & human-in-the-loop

AI PRD Architecture & AI UX Design

Session 13 - AI PRD Architecture

Product Development Lifecycle • Why PRDs exist • BRD vs PRD vs FRD • Traditional PRD anatomy • Where AI PRDs differ

Session 14 - AI UX Design

AI UX principles - designing for trust, transparency & control • Explainability & confidence: helping users understand AI decisions • The HAX Toolkit - Microsoft's Human-AI Interaction guidelines, applied • Common AI UX failure patterns (over-automation, invisible errors, lack of feedback) • Hands-on: running a HAX Toolkit evaluation on a real product

Responsible AI, AI Strategy & Feasibility/Impact Analysis

Session 15 - Responsible AI + AI Strategy (GTM, Moats & Roadmap)

Responsible AI as a strategic input - bias, fairness & transparency risk in your roadmap • The EU AI Act as a GTM constraint, not a side topic • Building responsible-AI checkpoints into GTM strategy • Identifying moats for an existing business - and where responsible-AI practice becomes the moat • Writing AI roadmaps with guardrails built in from the start

Session 16 - Feasibility/Impact Analysis

Prioritising AI use cases using feasibility/impact analysis • Technical feasibility vs business impact - the 2x2 framework • Scoring criteria: data readiness, cost, risk, and expected value • Live session: running feasibility/impact analysis with an actual client • Translating the analysis into a go/no-go recommendation

Data Strategy, Readiness & Designing AI Systems

Session 17 - Data Strategy and Readiness

Why data strategy matters • PM vs data engineer responsibilities • Types of context • Data procurement & readiness • Golden data & storage

Session 18 - Designing AI Systems

AI system design for PMs (technical, hands-on) • Automation vs ML vs GenAI vs Agent - decision framework • Real-life case studies from AtliQ's PM & AI team • Production trade-offs for AI PMs • Build vs Buy vs Partner • Reading model benchmarks • Multimodal AI • Cost vs Quality vs Latency

Mock Interviews, Career Readiness & Capstone Demo Day

Session 19 - Mock Interviews + Career Readiness + The Complete AI PM Recap: From Fundamentals to First 90 Days

Live mock interview practice • Practising case studies related to PM interviews • Career readiness for AI PM roles • How to write an ATS-friendly resume for AI PMs • The Complete AI PM Recap: From Fundamentals to First 90 Days (~45 mins) • Open Q&A

Session 20 - Demo Day

Final capstone presentations (top 3 submissions) • Expert panel review • Product feedback • Graduation & next steps

Who is in the room

Taught by people who build and ship AI products, not people who describe them.

Dhaval Patel

Founder, Codebasics · Ex-NVIDIA

Built AI products at NVIDIA. Teaches the core AI fundamentals and RAG architecture.

Karandeep Grover

CEO, AtliQ Technologies · Fractional CAIO

Has advised 55+ startups and enterprises on AI, tech and product. Teaches AI opportunity mapping and a problem-first mindset.

Akshay Seth

AVP, Product (AI & SaaS), PayMe

11+ years in fintech and product, 15+ AI products shipped. Teaches AI product and business metrics.

Aakash Pardeshi

AI PM Consultant · Ex-6sense, Hexaware

13+ years in product management, leading AI-driven B2B SaaS and fintech products. Teaches cost estimation and unit economics.

What learners say

Verified reviews from the product managers who went before you.

Every review below is published by a real Codebasics learner on our platform.

AI Product Manager cohorts Rated 5.0

From 53 verified ratings across cohorts 1 and 2: product managers, program leads, consultants and directors.

Najabat Ali Khan

Program Delivery Lead · Cohort 2

★★★★★

I joined the Codebasics AI Product Management cohort because I wanted something practical, not a regular PM course with AI added on top. What stood out for me is how AI is built into the whole product journey, from discovery and AI opportunity mapping to metrics, evals, cost, guardrails and GTM. The instructors are industry practitioners and that keeps the learning grounded in how products are actually built. The breakout sessions were equally valuable. Working through real problems with professionals from diverse domains brought different perspectives and made the learning much more practical. One idea that really stayed with me is simple. Start with the problem, not the AI. Understand the workflow and pain points first then use AI opportunity mapping to decide where AI genuinely adds value. The capstone brings everything together. It is not just about building an AI app but about working through the full AI product journey, from problem discovery and product definition to cost, metrics, evals, guardrails, a working prototype and stakeholder presentation. Overall, it has been a practical and highly structured way to learn how to think like an AI product manager, not just how to build with AI.

AI Product Manager

Abhishek Timmaraju

Senior Director IT Operations · Cohort 1

★★★★★

I was part of the pilot batch of the Codebasics AI Product Manager program, and it was one of the best online learning experiences I’ve had. I’ve taken courses from well-known universities, and the teaching here wasn’t merely comparable—it was better. The program gets the technical depth exactly right. It doesn’t turn product managers into engineers, but it gives them enough understanding to make informed decisions and have credible conversations with technical teams. Having also completed the Codebasics AI Engineering Bootcamp, I could clearly see how thoughtfully the two programs draw that boundary. What stood out most was how much Dhaval, Akshay, Karan, Hem, and the entire team cared about whether we genuinely understood the material. Every concept was made practical: building agents in different ways, evaluating costs, turning an idea into a defensible business case, and adapting the conversation for an AI engineer, CIO, or business stakeholder. The role-plays were especially valuable. When a topic moved too quickly, the team listened and arranged an additional session. That responsiveness, combined with their openness, curiosity, and respect for the cohort’s experience, made the classes feel like genuine conversations. I finished the program with skills I could apply at work immediately. I highly recommend it to aspiring AI product managers, business analysts, project managers, and functional consultants who want practical AI literacy and the confidence to lead AI initiatives.

AI Product Manager

Rohit Doad

Sr. Consultant · Cohort 1

★★★★★

I enrolled in the AI PM Bootcamp expecting to learn general AI PM frameworks and terminology. What I actually walked away with was something more specific - a real mindset change in how I approach implementing AI in product problems, which is a different layer than regular PM work. The capstone project was the turning point. Instead of a hypothetical case study, I worked on a real business problem, which meant I had to make actual tradeoffs, not just theoretical ones. It also taught me where AI actually belongs in a product and where it doesn't - not every problem needs an AI solution, and knowing which is which matters more than the AI itself. The rest of it felt different too, mainly because it came from founders who've actually built and shipped AI products themselves. They weren't teaching from a textbook; they were sharing what they'd learned the hard way, mistakes included. That made me take every exercise more seriously - this wasn't theory, it was real lessons from people who'd actually been in the room when things worked and when they didn't. I came out of this bootcamp thinking like an AI product manager, not just knowing what one does.

AI Product Manager

Suman Gadwal

Technical Project Manager · Cohort 1

★★★★★

Finished the Codebasics AI Product Manager Bootcamp this cohort. The part I found most useful was being pushed to justify AI features with numbers: cost per query, evaluation sets, and a clear view of how the model fails. That is a different muscle from traditional product and program work, and it is hard to build from reading alone. Good cohort, practical assignments, and a lot of useful discussion. Recommended for anyone transitioning into AI product management.

AI Product Manager

Trishala Basti

Product Manager and Startup Founder · Cohort 1

★★★★★

One of the best-designed and best-delivered AI Product Management courses I have attended so far. Hats off to the entire team for the thought, planning, and effort that has gone into designing this learning journey. This is truly "AI Product Management in Practice," not just another theory-based course. I genuinely feel that if I hadn't taken this course, I wouldn't even have known that many of these topics existed. Several of them are rarely discussed in books, yet they become incredibly important when you're working on AI products. This course brought those hidden but essential concepts into the open. Each mentor brings a unique perspective, and together they make the course incredibly comprehensive. Thanks to all mentors and the team who made this course possible. I am truly grateful to have had the opportunity to be part of this course. In a way, I had manifested finding a course exactly like this—and I'm so glad it became a reality.

AI Product Manager

On video Watch

Hear it from them directly. Each one opens their full video review.

AI Product Manager Cohort 3 After Demo Day

Reviews open after Demo Day. This space is reserved for the product managers in Cohort 3.

The first review in this column could carry your name.

Cohort 3 starts Saturday, 24^th October. 20 live sessions, 10 weeks, one capstone.

Enroll now →

Before you decide

Three ways to learn this. Only one makes you ship.

	Self-study	Generic AI course	This cohort
Live, with instructors	No	Rarely	20 live sessions and office hours
What you produce	Notes	A certificate	8 builds and a capstone
Real business exposure	None	Rarely	AtliQ case studies and a live client session
Evals, pricing and unit economics	On your own	Often skipped	Dedicated sessions
Building with AI	On your own	Slides only	A RAG chatbot and an agent, vibe coded
Investment	Your time	Usually far higher	US$630 inner circle

Questions

The things people ask us before they join.

Is this designed for fresh graduates?

It is built for working professionals: product and project managers, founders, engineers, business analysts, data professionals and consultants. No prior AI expertise is needed. If you are a fresh graduate, start with our recorded courses first.

Do I need coding experience?

No prior coding is needed. You vibe code a RAG chatbot and an AI agent in class with the instructor, using AI tools like Claude Code, LangChain and Groq.

Do you offer job placement?

We don’t promise jobs. You leave with 8 practical AI PM artifacts, working AI prototypes, and a capstone that showcases how you think, build, and make product decisions. Outcomes follow effort.

What is the refund policy?

If you register and it's not what you expected, you have until the end of Monday, 26th October 2026 to request a 100% refund, no questions asked.

How is this different from recorded AI PM content available online?

This is a 10-week live cohort in association with AtliQ Technologies. With direct mentor access, office hour sessions, real client interactions, and a Demo Day capstone you actually build. Different format, different outcome.

When are the live sessions held?

Saturdays and Sundays, 5 PM to 8 PM IST. Three hours per session: 2 hours of learning, a 30-minute break and 30 minutes of doubt clearing. Cohort 3 starts Saturday, 24th October 2026.

What happens after 10 weeks?

You keep 1 year access to all recordings. You join the alumni network.

What if I miss a live session?

Every session is recorded and available within 4–6 hours after it ends, and you keep access for 1 year. We still recommend attending live, as the live interactions and breakout room activities are where most of the learning happens.

Can I join after the sessions are started?

Yes, until Saturday, 31st October 2026. Recordings go up within 4 to 6 hours of each session, so you can catch up on week 1.

What is the Soft Skills Bootcamp and how do I access it?

The Soft Skills Bootcamp is a separate Codebasics program covering professional communication, executive presence, workplace influence, and stakeholder management - the fundamentals that make technical people effective leaders. Once you enroll in the Cohort, access is included automatically. Log into your Codebasics dashboard, go to your AI PM Cohort section, and you will find the Soft Skills Bootcamp courses listed there alongside your cohort materials. No separate purchase or enrollment needed.

I already purchased the Soft Skills Bootcamp or a course that is part of it. Do I get a price reduction on the AI PM Cohort?

Yes. If you have already paid for the Soft Skills Bootcamp or any individual course that is a part of it, the amount you paid for that purchase will be deducted from your Cohort fee at checkout.

Do I need to pay for the tools used in class?

The tools used in class, such as Miro, LangChain, Groq, Streamlit, and Vercel, have free tiers that are enough for learning and building. However, you will need an active Claude Pro plan (approx. $17 per month) to fully participate in the vibe coding and AI agent exercises during the cohort. If you later choose to build beyond the free-tier limits, you may need paid services or plans. These additional costs are optional and are not required to complete the cohort.

What is the Inner Circle, and how is it different from regular enrollment?

The inner circle is early enrollment for Cohort 3 at ₹36,000, open until Tuesday, 20th October 2026. After that the price is ₹48,000 until enrollment closes on Saturday, 31st October 2026. The cohort starts Saturday, 24th October 2026.

Cohort 3 · Starts Saturday, 24^th October

Ten weeks from now, one of those two lines is yours.

20live sessions
8builds
1capstone

Inner circle price

US$630

Closes Tuesday, 20^th October. Then US$840.

Enroll now →

Full refund until Monday, 26^th October

US$630 inner circle Closes Tue, 20^th Oct, then US$840

Enroll now →