The Ultimate AI PM Learning Roadmap
An extended edition with dozens of AI PM resources: definitions, courses, guides, reports, tools, and step-by-step tutorials
Hey, welcome to the special free edition of The Product Compass newsletter.
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In this issue I cover everything you need to know as an AI Product Manager.
It's an extended version of the post I recently published on social media with many new tools and resources.
If I had to learn AI Product Management again, I would start here:
1. Basic Concepts
Start with understanding what an AI Product Manager is.
Next, for most PMs, it makes no sense to dive deep into statistics, Python, or loss functions. Instead, you can find the most important concepts here: Introduction to AI Product Management: Neural Networks, Transformers, and LLMs.
[Optional] If you want to dive deeper, I recommend you check out an interactive LLM visualization:
[Optional] Finally, as an AI PM you will most likely work with LLMs, as they are the most cost-effective. But just in case, here are 8 other terms you might come across, explained by Generative AI on LinkedIn:
LLM (Large Language Models): Great for natural language understanding and generation (think ChatGPT).
LCM (Latent Concept Models): Powerful in capturing nuanced concepts hidden in data.
LAM (Language Action Models): Designed to not just understand, but also take action based on language input.
MoE (Mixture of Experts): Smartly combines expertise from multiple specialized models for superior performance.
VLM (Vision-Language Models): Handles text AND images, bridging visuals and language seamlessly.
SLM (Small Language Models): Ideal for efficiency and speed, especially in resource-constrained environments.
MLM (Masked Language Models): Masters context, great at predicting masked or missing content in text.
SAM (Segment Anything Models): Perfect for precise image segmentation and detailed visual understanding.
Before we proceed, I’d like to recommend the AI Product Management Certification:
I participated in this 6-week cohort in Spring 2024. I particularly loved networking and rolling up my sleeves.
The next session starts on July 13, 2025. I secured a $500 discount for our community if you use this link to sign up:
2. Prompt Engineering
52% of U.S. adults use LLMs. But very few know how to write good prompts.
I recommend starting with resources curated specifically for PMs:
[Optional] Other generic, free resources:
Guides:
An Awesome Analysis: System Prompt Analysis for Claude 4
Tools:
Anthropic Prompt Generator: Improve or generate any prompt
Anthropic Prompt Library: Ready-to-use prompts
Free, Interactive Course: Prompt Engineering By Anthropic
3. Fine-Tuning
Use those platforms to experiment with training and validation data sets and parameters such as epochs. No coding:
OpenAI Platform (start here, my favorite)
LLaMA-Factory (open source, lets you train and fine-tune open-source LLMs)
You can practice fine tuning by following this practical, step-by-step guide: A Practical Guide to Fine-Tuning for Product Managers
4. RAG (Retrieval-Augmented Generation)
RAG, by definition, requires a data source plus an LLM. And there are dozens of possible architectures.
So, rather than studying artificial names, I recommend the following resources to learn RAG in practice:
How to Build a RAG Chatbot Without Coding: A simple exercise step-by-step
See the point ‘Three Essential Agentic RAG Architectures’ from AI Agent Architectures, where I also comment on how to generalize that knowledge
5. AI Agents & Agentic Workflows
AI agents are the topic you can learn best by doing.
My favorite tool, by far, is n8n, that allows you to:
Create complex workflows with a drag-and-drop interface.
Easily integrate with dozens of systems (Google, Intercom, Jira, SQL, Notion, etc.).
Create and orchestrate AI agents that can use tools and connect to any MCP server.
You can start with those guides:
MCP for PMs: How To Automate Figma → Jira (Epics, Stories) in 10 Minutes (Claude Desktop)
J.A.R.V.I.S. for PMs: Automate Anything with n8n and Any MCP Server
AI Agent Architectures: The Ultimate Guide With n8n Examples
[Optional] And here are my favorite, free generic guides and reports:
Google Agent Companion: focuses on building production-ready AI agents
6. AI Prototyping & AI Building
I listed many tools, but in practice, Lovable, Supabase, GitHub, and Netlify are 80% of what you need. You can add Stripe. No coding.
Here are four practical tutorials:
How to Quickly Build SaaS Products With AI (No Coding): Introduction
A Complete Course: How to Build a Full-Stack App with Lovable (No-Coding)
[Optional] If you want to build and monetize your products, e.g., for your AI PM portfolio:
When building, focus on the value, not hype. Customers couldn’t care less about whether your product uses or has been built with AI.
7. Foundational Models
My favorite models (May 28, 2025):
Claude for coding (despite popular claims Gemini is better)
ChatGPT for everything else (that has recently changed)
8. AI Evaluation Systems
You might have the most advanced architecture. But the real question is this: Does your product actually work?
Evals are the most critical element. And it's a task also for PMs.
A free, detailed guide: Mastering AI Evals: A Complete Guide for PMs
9. Other Resources
A few other AI PM resources that I found particularly useful over the last months:
Anthropic MCP Servers: The official collection by Anthropic
Optional resources:
Awesome Generative AI Guide (GitHub): Updates, news, and materials
ChatLLM: Use all LLMs with just one $10/month subscription (I’m not affiliated)
MCP.so: The largest collection of MCP servers
microsoft/markitdown: For converting docs to Markdown (if your LLM needs to process documents)
Visual Summary
Plus, our practical, step-by-step guides available in the archive, AI Product Management:
Thanks for Reading The Product Compass Newsletter
Hope that helps!
It’s great to explore, learn, and grow together.
Have a great rest of the week ahead,
Paweł
That's a great overview.
Very nicely organized.