CHAPTERS
Why AI Product Managers are in high demand (and highly paid)
Aakash and Pawel open by discussing the rapid growth of the AI market and why AI PM compensation is often higher than traditional PM roles. The core reason: AI PMs must blend strong business/product judgment with enough technical fluency to collaborate effectively with engineers in a fast-moving space.
Prompting as a foundational AI PM skill: context + structure beats casual prompts
Pawel demonstrates a high-quality prompt aimed at uncovering hidden assumptions in continuous product discovery. He emphasizes giving rich context and explicitly specifying the thinking steps and outputs to produce more reliable, actionable results from LLMs.
Prompting “hacks” and best practices PMs can apply immediately
They expand from the demo into repeatable prompting techniques that improve output quality and reduce bias. Pawel shares practical tactics like setting success criteria, using templates, iterating, and even adding “rewards” to increase detail in responses.
AI PRDs: aligning strategy and avoiding “AI for AI’s sake”
Pawel explains what changes in an AI PRD versus a standard PRD, developed with input from an OpenAI product lead. The PRD’s main job is organizational alignment and surfacing assumptions—especially important amid AI hype and pressure to build AI features without a business case.
AI PRD template walkthrough: market, users, value, defensibility, scope, GTM
They outline the major sections of Pawel’s AI PRD template and how each section helps validate the opportunity and execution approach. The template emphasizes segmentation, value proposition clarity, sustainable competitive advantage, evaluation considerations, and go-to-market planning.
Fine-tuning demo: when prompts are too heavy and style/behavior must be consistent
Pawel shows why repeatedly injecting long “style prompts” is costly and unreliable, using a clone example. He demonstrates supervised fine-tuning on OpenAI (Yoda-style dataset) to internalize behavior into model weights, enabling cheaper models and shorter prompts for repeated use cases.
Evaluating fine-tunes: reading training/validation curves and knowing when to stop
They interpret the fine-tuning dashboard outputs, including training loss trends and validation checkpoints after each epoch. The takeaway is practical: you often reach “good enough” quickly, and extra epochs can be wasteful or even harmful.
RAG explained: answering from many documents without paying to stuff them into prompts
Pawel builds a RAG chatbot over a Google Drive folder of articles to demonstrate retrieval at scale. He explains why you can’t just paste lots of documents into the prompt (cost and scale) and introduces vector databases as the practical solution.
Building a RAG pipeline live: Google Drive → n8n workflow → Pinecone embeddings
They implement an ingestion workflow: detect new files in a Google Drive folder, download them, chunk them, embed them, and store them in Pinecone with metadata. The live build includes real-world iteration/debugging, reinforcing how these systems are developed in practice.
RAG query endpoint: webhook retrieval + aggregation + LLM response with citations
Pawel creates a webhook endpoint that takes a user query, retrieves top-k similar chunks from Pinecone, and sends them to an LLM to generate an answer grounded only in retrieved context. The response includes cited sources using stored metadata (document names).
Shipping a simple RAG chatbot UI quickly with Lovable (and common pitfalls)
They use Lovable to generate a lightweight chatbot frontend that calls the webhook and renders responses. The demo highlights how fast prototyping can be, while also showing typical issues like inconsistent markdown rendering and the need for iterative prompting/examples.
MCP in practice: agents reading Figma and creating Jira epics/stories automatically
Pawel introduces MCP (Anthropic’s standard) as a way for agents to discover and use tool capabilities via consistent interfaces. He demos Claude connecting to Figma and Jira via MCP servers to generate epics and INVEST-style user stories with acceptance criteria from a design file.
AI agents and orchestration: deep market research + tool-using agents in n8n
They close with AI agents as the top of the “AI PM pyramid,” including a deep market research agent that plans tasks, delegates to sub-agents, gathers sources, and synthesizes product-focused insights. Pawel also shows how n8n agents can use tools (email, calendar, Jira) to execute multi-step actions, emphasizing orchestration patterns and common setup gotchas.
