From prompting through AI PRDs, fine-tuning, RAG, MCP, and AI Agents, today's episode is a complete crash course on how to become an AI PM. Trailer - 00:00 Why AI PMs Are Paid So Much - 1:25 Effective Prompting for AI PMs - 02:39 Ad: Linear - 09:57 Ad: Miro - 10:42 AI PRD Template - 11:54 Fine-Tuning vs RAG - 16:42 Ad: Amplitude - 19:01 Fine-Tuning Demo: Creating a Yoda-Style AI Assistant - 19:52 RAG Implementation: Connecting Documents to AI Chatbots - 30:03 MCP (Machine-Callable Programs): Working with Multiple Tools - 59:00 AI Agents: Creating Advanced Product Research Assistants - 01:18:31 Future of AI Product Management - 01:33:16 Outro - 01:35:49 💼 Check out our sponsors: Linear: Plan and build products like the best - https://linear.app/partners/aakash Miro: The innovation workspace - http://miro.pxf.io/PO4WZX Amplitude: Try their 2-minute assessment of your company’s digital maturity - https://bit.ly/4hl25RG 👀 Where to find Pawel: LinkedIn: https://www.linkedin.com/in/pawel-huryn Newsletter: https://www.productcompass.pm YouTube: https://www.youtube.com/@pawelhuryn 👨‍💻 Where to find Aakash: Twitter: https://www.twitter.com/aakashg0 LinkedIn: https://www.linkedin.com/in/aagupta/ Instagram: https://www.instagram.com/aakashg0/ Transcript: https://www.news.aakashg.com/p/complete-course-ai-product-management 🔔 Subscribe and like the video to support our content! 🔑 Key Takeaways 1. Prompting isn’t a Trick, it’s the Product. Prompting isn’t something you tack on at the end…It’s a core part of how the product works.Well-structured prompts completely change the quality of output. It’s basically the UX layer for LLMs. Your goal isn’t to outsmart the model but to teach it how to behave with clear, repeatable instructions. 2. RAG is How You Stop Hallucinations And Keep Your Product Fresh! Instead of cramming everything into the model or relying on fine-tuning, Retrieval-Augmented Generation (RAG) lets you pull in the right context when you need it. For example, he used it to pull product changelog data and get accurate responses… Without needing the model to already “know” that info. If your product updates often, RAG keeps the AI current without hardcoding anything. This is how you reduce hallucinations and keep things adaptable. 3. Most PMs fine-tune When They Should just Prompt Better. He has seen this mistake countless times: PMs reach for fine-tuning too early. He showed a side-by-side of zero-shot, few-shot, and a fine-tuned model.All summarizing a product dashboard.The few-shot prompt actually did better than the fine-tuned version. Most PMs go straight to fine-tuning, but with the right prompt structure, you can get 95% of the result!And it’s way faster, cheaper, and easier to maintain. 4. An AI Agent Is Just a Pipeline That Thinks The term “agent” gets thrown around a lot, but under the hood, it’s a system that can think: Intent classification, tool selection, execution logic, and error handling. If you don’t design for that structure, your agent becomes unpredictable fast. The real magic happens when you coordinate its behavior with reliable systems thinking and that’s your moat! 5. AI PRDs Need a New Language! Traditional PRDs were built for deterministic systems.You specify inputs, define expected outputs, and call it done. You’re not writing “requirements”, you’re writing intent, behavior, and expected failure modes. Here’s how to write PRDs for AI products: → Include structured prompts, not just user flows→ Provide real input/output examples→ Define what “acceptable variance” looks like→ Plan for fallbacks, retries, and recovery UX Most importantly: You’re not managing the model, you’re collaborating with it. And if your PRD doesn’t reflect that dynamic, your product will feel brittle, unpredictable, or worse… totally misaligned with user needs. #podcast #productmanagement #ai 🧠 About Product Growth: The world's largest podcast focused solely on product + growth, with over 167K listeners. Hosted by Aakash Gupta, who spent 16 years in PM, rising to VP of product, this 2x/ week show covers product and growth topics in depth.