
Master product design with ai by turning hundreds of pieces of user feedback into clear problems, prototype quickly with ai tools, and ship faster using rag and prompt engineering.
Explore how traditional product design maps user research, personas, journeys, and testing to business goals, and how AI augmentation accelerates pattern recognition, first draft generation, and information synthesis.
Explore how agent mode in ChatGPT accelerates product research by conducting competitive fintech onboarding analyses, grounding insights in real sources, and delivering ready-to-use briefs and presentations.
Explore what generative AI is and why it matters for product management. Learn three practical ways to use it: general-purpose tools, specialized PM tools, and in-house solutions.
Discover how Google AI Studio's build feature lets product managers turn feature descriptions and user research into a working app from natural language, accelerating problem discovery and iteration.
Discover how to apply generative AI to problem discovery, synthesizing unstructured user research with Claude for single sources and Notebook LM for multi-source work, with source citations.
Learn how Claude synthesizes 100 user reviews into themes, ranks them by frequency and emotion, and crafts structured problem statements with example codes for onboarding improvements.
Learn to synthesize research across five documents using Notebook LM to reveal cross-source themes in onboarding. The tool links findings to exact sources via citations, enabling traceable, evidence-based prioritization.
Learn to run basic generative AI code in Python using Google Colab by sending prompts to a large language model with an API key, building a simple AI app.
Explore logistic regression on product design data with plain-English prompts and AI tools like ChatGPT, Cloud, and Gemini, then build a decision tree and dashboard to interpret abandonment and usability.
Demonstrate how Microsoft Copilot enables product teams to analyze feedback data with plain-English prompts, generate pivot tables, compute days since submission, and clean submitter names—no Excel expertise required.
Ground AI responses in your own documents through retrieval augmented generation, breaking text into chunks, creating embeddings, and using a vector database to retrieve relevant information from a knowledge base.
Demonstrates a practical rag workflow in Python using LangChain and OpenAI, turning a design system document into chunks, embeddings, and a vector store to answer questions grounded in context.
Learn to run large language models locally with Olama, preserving privacy and avoiding subscriptions; install, download a model, and chat offline, exploring open source options and size trade-offs.
Discover how designer prompts expose onboarding and activation challenges, and how to leverage generative AI to improve user experience while guarding against bias, misuse, and data leakage.
Discover how bias and hallucination in generative ai can shape product design outcomes. Implement inclusive inputs, human review, ongoing monitoring, and retrieval augmented generation to ground ai in 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, UX designer, or aspiring builder who's heard "use AI in your workflow" a hundred times but still isn't sure how — this course is your answer.
Are you watching competitors ship faster, research deeper, and design smarter, while you're still copy-pasting user feedback into spreadsheets? Do you sense that AI is transforming product design but can't find a course that goes beyond surface-level tips and actually puts the tools in your hands? That gap ends here.
Product Design with AI is a hands-on, practitioner-focused course that takes you from AI-curious to AI-capable. Across 16 structured lectures, you'll move through the full product design lifecycle — research, discovery, prototyping, analysis, and responsible use — armed with the most powerful AI tools available today: ChatGPT, Claude, NotebookLM, Google AI Studio, Microsoft Copilot, and more. This isn't theory. Every concept is paired with real demonstrations, working code, and practical workflows you can deploy immediately.
In this course, you will:
Apply AI agents like ChatGPT and Claude to accelerate user research, problem discovery, and feedback synthesis
Build functional prototypes and lightweight apps using Google AI Studio — no deep coding background required
Execute multi-source research synthesis using NotebookLM to surface insights from messy, real-world data
Run basic generative AI code in Python via Google Colab, demystifying the technical layer of AI tools
Implement Retrieval-Augmented Generation (RAG) — the technique powering the smartest AI products — with a guided code walkthrough
Conduct advanced product data analysis using ChatGPT and Claude to make faster, evidence-backed decisions
Craft high-leverage prompts that function as product insights, not just chat queries
Navigate bias, hallucination, and data privacy risks so you can use GenAI responsibly in a professional setting
Why does this matter now?
AI isn't coming to product design — it's already here. Teams that know how to direct AI tools are doing in hours what used to take weeks. Whether you're conducting user interviews, synthesizing research, analyzing usage data, or scoping features, there is an AI workflow that makes you faster and sharper. The product managers and designers who thrive in the next five years won't be those who know the most about AI in the abstract — they'll be the ones who've actually used it, debugged it, and built systems around it.
What makes this course different?
This course doesn't just survey AI tools from the outside — it puts them in your hands. You'll complete video-guided demonstrations, quizzes that reinforce your mental models, and role plays that simulate real product design scenarios, so you're not just watching someone else use AI — you're practicing with it yourself. The curriculum covers both the creative side (research, ideation, synthesis) and the technical side (Python, RAG, local LLMs), giving you rare end-to-end fluency without requiring an engineering background.
Ready to design smarter? Enroll now and start building the AI-augmented product practice that your competitors wish they had.