This resource provides a 30-day roadmap to build a strong foundation in AI for Product Management. Each day includes a specific topic with one or two resources (articles, videos, or templates) to read or watch for 15 minutes.

The roadmap progresses from foundational concepts to practical skills, culminating in advanced topics and career preparation. A bonus resources section offers additional materials for deeper exploration.

How to Use This Roadmap

Daily Commitment: Spend 15 minutes each day on the assigned topic and resources.
Engage Actively: Take notes, reflect on how concepts apply to real products, and explore bonus resources if time allows.
Goal: By Day 30, you’ll have a solid understanding of AI Product Management principles, tools, and career paths.

30-Day Roadmap

Day 1: What is AI for Product Management?

Learn AI PM role: bridging technology and business value.

Resource 1: What is an AI product manager? (Article, 8 min)
Resource 2: "How to Become a Confident AI Product Manager?" by Product Space (Video)

Day 2: Evolution of AI

Understand AI's historical journey from concepts to modern applications.

Resource 1:What is the history of artificial intelligence (AI)? (Article, 5 min)
Resource 2: A Brief History of AI (Video, 7 min)

Day 3: Deep Dive: RNNs, CNNs, and Artificial Neural Networks.

Learn RNNs, CNNs, and neural networks for different data types.

Resource 1:Introduction to Recurrent Neural Networks (Article, 5 min)
Resource 2: What are Convolution Neural Networks by IBM (Video, 6 min)
Resource 3: Neural Networks Explained in 5 Minutes by IBM (Video, 5 min)

Day 4: Foundation Models and Generative AI

Understand foundation models and how generative AI creates new content.

Resource 1: Foundation Models in Generative AI Explained by Amazon (Article, 6 min)
Resource 2: Foundation Models & Generative AI. INTRODUCTION by MIT (Video, 47 min)

Day 5: Applications of Discriminative, Predictive, and Generative Models.

Learn discriminative, predictive, and generative models and their practical applications.

Resource 1: Decoding Generative and Discriminative Models (Article, 5 Min)
Resource 2: Practical Applications of Discriminative Models (Article, 6 min)
Resource 3: Generative vs. Discriminative AI (Video, 16 min)

Day 6: Ethics & Privacy in AI

Understand AI ethics, bias, privacy, and responsible development practices.

Day 7: LLM Fundamentals: What are LLMs?

Learn Large Language Models basics and their capabilities.

Day 8: Capabilities and limitations of LLMs.

Understand what LLMs can and cannot do effectively.

Resource 1: What Are the Limitations of Large Language Models (LLMs)? (Article, 5 min)
Resource 2: This is What Limits Current LLMs (Video)

Day 9: Fine-Tuning LLMs

Learn when and how to customize LLMs for specific tasks.

Resource 1: Fine-Tuning LLMs: A Guide With Examples (Article, 4 min)
Resource 2: When and Why to Fine Tune an LLM (Video)

Day 10: LLM Applications & Case Studies

Understand real-world LLM implementations and successful product examples.

Resource 1: Real-World Examples of LLM Applications and Case Studies (Article, 5 min)
Resource 2: Building Real-World LLM Products with Fine-Tuning (Video)

Day 11: LLM Training & Testing

Learn LLM training processes and evaluation methods.

Resource 1: LLM Training: How It Works and 4 Key Considerations (Article, 4 min)
Resource 2: Learning at test time in LLMs (Video)

Day 12: Exploring GitHub & Hugging Face.

Understand key platforms for AI model sharing and collaboration.

Resource 1: What is Hugging Face 🤗? - Hugging Face ML for Games (Article, 5 min)
Resource 2: Total noob's intro to Hugging Face Transformers (Article, 4 min)
Resource 3: What is GitHub? (Video)
Resource 4: About GitHub and Git (Documentation, 8 min)

Day 13: Data Preparation & Python Libraries for fine-tuning.

Learn data preparation techniques and tools for model customization.

Resource 1: Prepare Fine-tuning Datasets with Open Source LLMs (Video)
Resource 2: Advanced Data Prep and Visualisation Techniques (Video)
Resource 3: Preparing Datasets for Fine-Tuning ML Models (Article, 5 Min)

Day 14: Model Evaluation: ROUGE

Understand ROUGE metrics for evaluating text generation quality.

Resource 1: Understanding BLEU and ROUGE score for NLP evaluation (Article, 6 min)
Resource 2: What is ROUGE metric ? (Video)
Resource 3: Large Language Models Evaluation Metrics (Video)

Day 15: Prompt Engineering

Learn techniques for crafting effective AI model prompts.

Resource 1: Prompt Engineering Guide (Article, 9 min)
Resource 2: Prompt Engineering Guide for Product Managers (Article, 10 min)

Day 16: Hands-on Practice with prompt-engineering tools (OpenAI Playground)

Understand practical prompt engineering using OpenAI's development tools.

Resource 1: OpenAI Prompt Engineering Guide (Documentation, 12 min)
Resource 2: OpenAI Platform Playground (Documentation, 10 min)

Day 17: Prompting Techniques

Learn advanced prompting strategies for better AI responses.

Resource 1: Prompting Techniques (Article, 8 min)
Resource 2: 17 Prompting Techniques to Supercharge Your LLMs (Article, 10 min)

Day 18: Single/Multi-shot prompting with examples.

Understand zero-shot, one-shot, and few-shot prompting with examples.

Resource 1: Zero-Shot, One-Shot, and Few-Shot Prompting (Article, 6 min)
Resource 2: What is One Shot Prompting? by IBM (Article, 6 min)
Resource 3: Use examples (multishot prompting) to guide Claude's (Article, 10 min)

Day 19: Zero-shot prompting and RLHF explained.

Learn zero-shot prompting and Reinforcement Learning from Human Feedback.

Resource 1: What is zero-shot prompting? by IBM (Article, 7 min)
Resource 2: RLHF reward functions from BERT classifiers and Zero-Shot (Video)
Resource 3: RLHF & DPO Explained (In Simple Terms!) (Video)

Day 20: Chain of Thought vs Tree of Thought prompting.

Understand advanced reasoning techniques for complex AI problem-solving.

Resource 1: Tree of Thoughts (ToT) (Article, 8 min)
Resource 2: Chain-of-thought, tree-of-thought, and graph-of-thought (Article, 7 min)
Resource 3: Chain-of-thought prompting - Explained! (Video)

Day 21: Jailbreaks and prompt leaking.

Learn AI security risks: prompt injection and jailbreak attacks.

Resource 1: Prompt Injection vs. Jailbreaking: What's the Difference? (Article, 6 min)
Resource 2: Navigating LLM Threats: Detecting Prompt Injections (Video)

Day 22: GANs Explained: Generator vs Discriminator.

Understand Generative Adversarial Networks and their competing components.

Resource 1: Generative Adversarial Network (GAN) by Geeks for Geeks (Article, 7 min)
Resource 2: The Discriminator | Machine Learning by Google (Article, 7 min)
Resource 3: The Generator | Machine Learning by Google (Article, 6 min)
Resource 4: Generator vs Discriminator in GAN - Basics of GAN (Video)

Day 23: Synthetic media and marketing with GANs.

Learn GAN applications for creating synthetic content and marketing.

Resource 1: What is Synthetic Media? AI, Gans, & Digital Content (Article, 9 min)
Resource 2: Synthetic Data Generation Using GANs (Article, 6 min)
Resource 3: Generating Synthetic Data With GANs (Video)

Day 24: RAG & Agentic AI

Understand Retrieval Augmented Generation and autonomous AI agent systems.

Resource 1: What is Agentic RAG? (Article, 8 min)
Resource 2: The Future of RAG is Agentic - Learn this Strategy NOW (Video)
Resource 3: What is Agentic RAG? (Video)

Day 25: Use of RAG

Learn practical RAG applications for enhanced AI responses.

Resource 1: 7 Practical Applications of RAG Models and Their Impact (Article, 9 min)
Resource 2: How to use Retrieval Augmented Generation (RAG) (Video)
Resource 3: AI Explained: What is RAG - Retrieval Augmented Generation? (Video)

Day 26: Limitations & Advantages of Rag

Understand RAG benefits and challenges in real implementations.

Resource 1: The Limitations and Advantages of Retrieval Augmented (Article, 8 min)
Resource 2: RAG architecture - LLM Hallucination Risks and Prevention (Article, 7 min)
Resource 3: 10 Challenges in Building RAG-Based LLM Applications (Video)

Day 27: AI Feasibility for Product Features

Learn to assess AI feature feasibility, desirability, and viability.

Resource 1: Managing AI Products: Feasibility, Desirability and Viability (Article, 6 min)
Resource 2: How to Assess AI Feasibility: A Product Manager's Guide (Article, 8 min)

Day 28: Human-in-the-Loop (HITL)

Understand integrating human oversight in AI product workflows.

Resource 1: What is Human-in-the-Loop (HITL) in AI & ML? by Google (Video)
Resource 2: Human-in-the-Loop in Machine Learning (Article, 6 min)

Day 29: AI Risk Management & Product Strategy

Learn AI risk frameworks and strategic product considerations.

Resource 1: AI Risk Management: Effective Strategies and Framework (Article, 6 min)
Resource 2: AI Risk Management Framework (Article, 7 min)

Day 30: AI Project for Product Managers

Understand practical AI tools and insights for product management.

Resource 1: AI for Product Managers: Practical Tools and Insights (Video)
Resource 2: Make product management fun again with AI agents (Article, 6 min)

Next Steps

Reflect: Review your notes from the 30 days and identify areas to dive deeper into (e.g., LLMs, RAG, Agentic AI or Prompting).
Practice: Apply your knowledge by creating a sample roadmap or PRD using Notion templates.
Network: Join PM communities to connect with mentors and peers.
Career Prep: Build a portfolio and practice mock interviews to land your first AI PM role.

Congratulations on completing the 30-day roadmap! You’re now equipped with a strong foundation in AI Product Management. Keep learning and building!