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 freearchived edition of The Product Compass newsletter.

Every week, I share actionable tips, templates, resources, and insights for PMs.

Consider subscribing or upgrading your account for the full experience:

---

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:

Source: 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:

1. LLM (Large Language Models): Great for natural language understanding and generation (think ChatGPT).

2. LCM (Latent Concept Models): Powerful in capturing nuanced concepts hidden in data.

3. LAM (Language Action Models): Designed to not just understand, but also take action based on language input.

4. MoE (Mixture of Experts): Smartly combines expertise from multiple specialized models for superior performance.

5. VLM (Vision-Language Models): Handles text AND images, bridging visuals and language seamlessly.

6. SLM (Small Language Models): Ideal for efficiency and speed, especially in resource-constrained environments.

7. MLM (Masked Language Models): Masters context, great at predicting masked or missing content in text.

8. SAM (Segment Anything Models): Perfect for precise image segmentation and detailed visual understanding.

Source: Generative AI

---

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:

• Top 9 High-ROI ChatGPT Use Cases for Product Managers

• The Ultimate ChatGPT Prompts Library for Product Managers

• See the point ‘Eleven AI Agent Prompting Principles’ from AI Agent Architectures

[Optional] Other generic, free resources:

• Guides:

  • GPT-4.1 Prompting Guide

  • Anthropic Prompt Engineering

  • Prompt Engineering by Google

• 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)

• Hugging Face AutoTrain

• 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)