
Explore generative AI for product management with Monika Rawat, featuring hands-on practice with AI tools, RAG, local LLMs, and prompt engineering for user research, discovery, and design.
Explore how generative AI empowers product management with context-aware outputs, from analysis to problem statements. Choose from three practical paths: browser tools, specialized product management tools, or in-house apps.
Explore agent mode in ChatGPT for product managers, enabling real-time research, competitive onboarding analysis, and deliverables like a PowerPoint deck and structured problem statements grounded in current sources.
Learn how product managers turn feature descriptions and user research documents into a working app with Google AI Studio's Build, accelerating prototyping without writing code.
Leverage generative ai for problem discovery by synthesizing unstructured user research with Claude and Notebook LM, enabling multi-source insights and source-cited results.
Learn how Claude analyzes 100 user reviews to cluster onboarding issues, rank themes by frequency and emotion, and turn them into actionable, user-centric problem statements.
Synthesize cross-document research with Notebook LM to unify five sources—user interviews, support tickets, stakeholder notes, and a competitive teardown—into evidence-backed onboarding insights with traceable citations.
Learn to interact with AI models via APIs using an API key in Google Colab by sending prompts to a large language model and generating a structured product design brief.
Learn to use Claude, ChatGPT, and Gemini to analyze product design data in plain English, with logistic regression, decision trees, and dashboards guiding your product roadmap.
Watch how Microsoft Copilot analyzes product feedback data in Excel with English prompts, generating pivot tables, calculating days since submitted, and formatting submitter names in square brackets with user segments.
Discover retrieval augmented generation (RAG) and how grounding AI responses in your own documents improves reliability, using document chunking, embeddings, and a vector database with LangChain.
Build a simple rag system in Python using embeddings and an OpenAI LLM with LangChain; chunk the design system, index with embeddings, retrieve content, and generate answers via a prompt.
Install OLAMA, download a model, and run a local LLM on your system. Experience privacy and cost benefits by keeping data on premises and using offline, open-source models.
Analyze prompts and AI interactions to uncover product insights, protect confidential information, prevent bias and misuse of AI tools, and guide onboarding and UX improvements in AI-driven product design.
Tackle bias and hallucination in generative AI to safeguard users in product design. Apply inclusive inputs, human review, monitoring, and retrieval augmented generation to ground AI in verified design knowledge.
This course contains the use of artificial intelligence*.
(*Some images used in this course have been created using artificial intelligence (AI) for illustrative and educational purposes. These images are not real photographs of actual people, organisations, or events and are intended solely to support learning and understanding.)
If you're a Product Manager who feels like AI is moving faster than you can keep up — and you're worried about being left behind — this course was built for you.
Are you tired of hearing about AI's potential but struggling to apply it to your actual product workflow? Do you want to go beyond the buzzwords and start using Generative AI to make smarter, faster, and more impactful product decisions?
This course is your hands-on, practitioner-first guide to integrating Generative AI into every stage of the product lifecycle. From user research and discovery to building lightweight apps and working with cutting-edge tools like RAG and local LLMs, you'll walk away with real skills — not just theory.
In this course, you will:
Apply ChatGPT, Claude, and Google AI Studio to real product management workflows — from ideation to delivery
Execute AI-powered user research and synthesize large volumes of customer feedback in minutes using tools like NotebookLM
Build AI-enhanced applications and run GenAI Python code in Google Colab — no deep technical background required
Implement Retrieval-Augmented Generation (RAG) with practical code walkthroughs designed specifically for PMs
Run local Large Language Models using Ollama and understand when and why that matters for your product
Master prompt engineering techniques that unlock deeper product insights from any AI model
Navigate the critical challenges of hallucination and bias so you can responsibly ship AI-powered products
Generative AI isn't a future skill — it's a present-day competitive advantage. PMs who can fluently work alongside AI tools are already shipping better products, running faster discovery cycles, and making more confident, data-backed decisions. This course equips you to be one of them.
Throughout the course, you'll complete hands-on activities including live tool demos, code walkthroughs in Google Colab, real-world research synthesis exercises, and RAG implementation projects — all grounded in the day-to-day realities of product work.
What sets this course apart is its unapologetically practical approach. Every lecture is designed around tools you can open today and workflows you can plug into your product process tomorrow. There's no fluff, no endless slides — just real use cases, real code, and real outcomes for PMs at any technical level.
Enroll now and start building the AI-powered product instincts that will define the next generation of great PMs.