AI Product Management – Learn with Me Series

A step by step structured approach from Foundational AI Concepts to Advanced Agentic Workflows

Welcome to my “AI Product Management – Learn with Me Series.”

Let me be upfront: I’m not an AI expert. I’m here to share my journey, not to sell you the illusion of mastery in a field that evolves faster than we can keep up.

I’m a product manager who has spent 8+ years navigating technical product management roles without a coding background. My journey has always been fueled by curiosity, logic, and analogy—skills that have helped me bridge the gap between technical complexity and business outcomes.

As AI transforms industries, I've come to understand that staying relevant requires more than just keeping up; it demands a commitment to continuous learning, unlearning, and experimentation.

These pages represent my personal AI learning sandbox.

Here, I document the skills I’m mastering, the frameworks I'm developing, and the insights I'm uncovering as I upskill in AI product management.

This isn’t a static resource or a rigid playbook. It’s a dynamic guide—designed to grow, adapt, and evolve alongside the advancements in AI.

If you’re here, it’s likely because you share my passion for staying ahead in building next-generation products.

What You’ll Find Here

This pinned post outlines a thoughtfully structured curriculum divided into tDifferent Themes. Each level progresses from foundational AI concepts to advanced agentic workflows, ensuring that traditional product managers transitioning into AI roles have a clear roadmap.

This space is dedicated to exploring the nuances of AI product management and breaking down complex topics into actionable insights

Key Outcomes

✅ Strong foundation understanding of AI product development

✅ Confident transition to AI product management

✅ Practical skills for immediate application

✅ Clear frameworks for decision making

✅ Ability to lead AI initiatives effectively

This structure serves as my personal roadmap for what I aim to learn. This roadmap evolves as I uncover new questions, and explore answers to them.

As I navigate this journey, I leverage various resources, professional experiences, and the new questions that arise along the way.

In addition to AI product management, I also write about personal growth and leadership, but right now, I’m focusing on honing my niche skills in AI product management—an area that’s on my radar for 2025.

Whenever I uncover insights or answers, I make it a priority to share them with all of you. And don’t worry if you see many posts labeled “coming soon.” I take the time needed to ensure that each post is authentic and of the highest quality, designed to deepen our collective understanding.

Get started and click the links for posts marked with a ✅ today.

MY PERSPECTIVES ON EVOLVING AI LANDSCAPE

• What is the imperative of AI Upskilling for PMs? ✅

• Why is AI Product Management different from Traditional PM? ✅

• What DeepSeek Can Teach Product Managers About AI? ✅

• Is the AI’s trillion-dollar promise hype or reality? ✅

• The Evolution of PMs in the AI Era ✅

• How can PMs evaluate which features need data science, ML, or generative AI to confidently pick the exact AI solution that aligns with user needs, budget, and ethical considerations? ✅

• Model Context Protocol (MCP): The Universal Translator That Will Redefine our AI Products ✅

Coming soon -

• Multi-Agent GenAI: The Next Trillion Dollar Opportunity

• Different types of AI PM roles

• Deep Research - Comparisons across various providers

• What are the key security & privacy concerns in AI systems for individuals and enterprises?

• Responsible AI Frameworks – Ethics, Bias Detection, Governance

• What puzzles do we face with AI making its own content? What are the game's rules when AI’s creations cross paths with copyright laws?

• How can society adapt to the ethical challenges posed by Generative AI?

---

1. AI PM 101 - Understanding the Basic Definitions

• What is the difference between Generative AI vs. Traditional AI? ✅

• How does Generative AI produce its outputs? ✅

• What is an LLM? ✅

• What is Retrieval-Augmented Generation (RAG)? ✅

Coming Soon -

• What is the difference between AI, ML, DL, and Gen AI?

• What is the difference between supervised, unsupervised, and reinforcement learning?

• Computational Concepts: Deterministic vs. Non-Deterministic Methods

• What is a Transformer? (Attention, Tokenization)

• What is a vector database?

• What are Neural Networks?

• What are embeddings?

• What is Prompt Engineering?

+ More

My favourite recommendations to develop a deep foundational understanding:

• Transformers (how LLMs work) explained visually (Must Watch & best ever explanation)

• Intro to Large Language Models by Andrej Karpathy

• Deep Dive into LLMs like ChatGPT by Andrej Karpathy

• Attention in transformers, step-by-step (by 3Blue1Brown)

• How might LLMs store facts

---

2. AI for Personal Productivity

• Understand Advance Prompting Techniques

Some really amazing online resources

• How I use LLMs by Andrej Karpathy

• How to use AI to 10x your productivity

Coming Soon -

• What are some of the amazing AI tools available for a Product Manager today?

---

3. Understanding the Technical Foundations of Generative AI & Agentic Workflows

Foundation Tech

• Data Strategy: Building the Data Moat Your AI Product Needs ✅

• Essential Data skills for an AI PM ✅

• Part #1 ✅

  • Data Ingestion & Preparation

  • Data Labeling & Synthetic Data

  • Compute Infrastructure

  • Vector Databases

  • Orchestration Frameworks

• Part #2 ✅

  • Foundation Models

  • Model Supervision & Observability

  • Model Safety & Responsible AI (Quality, Bias, Interpretability)

  • Agent Architectures & Tooling

Advanced Tech

• What is Graph RAG? Why Product Managers Should Care ✅

Coming soon -

• What factors affect the scalability of AI systems?

• Discuss the role of distributed computing in scaling AI applications.

• Model Versioning – Manage model updates without disrupting users

• What are the components of AI infrastructure necessary for deploying AI models

• Explain challenges and solutions for managing data throughput in high-velocity AI systems.

• How do you approach optimizing AI model inference times for low-latency applications?

• What strategies can be employed to reduce the computational cost of training large AI models?

• AI Pipeline Optimization (MLOps) – Optimize complex AI workflows for reliability, cost, and performance

+More

Multi-Agent Workflows

• What is an AI Agent and the difference between AI Agents vs. AI Assistants?

• Agent Opportunities – Identify where autonomous agents can transform your product

• Understand an Agent Capabilities – Designing agents that can solve complex user problems effectively

• What are the architecture options for Agentic Workflows?

• Cross-Model Orchestration – Combining multiple AI models for sophisticated capabilities

• Practical Implementation Guide for Multi-Agent Systems

+More

---

4. Product Strategy, Building & User Experiences in an AI-powered World

AI-powered Discovery & Research

Coming soon -

• Leveraging cutting edge models Deep Research capabilities

• Synthetic User Research

• Analyzing User Feedback with AI

AI-powered Strategy Building

• The 10× PRD Framework – Building AI Products That Scale ✅

• AI Economics – Mastering the Money Math Behind Scalable AI Investments ✅

AI Prototyping

Coming soon -

Best Practices in building Prototypes leveraging cutting edge AI tools.

• Additional Resources (My favourite posts from brilliant practitioners)

  • How to Design & Build AI Prototypes with No-Code Tools

  • Build Apps With AI Without Getting Stuck | Colin Matthews

  • Amjad Masad (Replit)

  • Prototype 5 features in 84 mins (Bolt, Cursor, Lovable, Replit, v0)

AI powered Development

Coming soon -

• Model Selection – Balancing capability, cost, and control effectively

• Mastering the Unique Cycles of Improving AI Products (Beyond Traditional Agile)

+ More

AI/ML Evaluations (Evals) - Model Observability

Basic Evals Understanding:

Coming soon

• Advanced Evals

  • (I will share my learnings from Arize & Deep Learning online trainings)

---

Future Themes: Go-to-Market (GTM) with AI for Your Product

---

Bonus: Resources for AI Product Manager Interview Prep

This will be crucial apart from deep understanding. I want to use this space, to put my own interview perspectives and also share best guidance shared some of the deep AI practitioners like Dr. Marily Nika & like,

Resources

• How to Become an AI PM

• How to Become an AI PM in 2025

• How To Get FAANG AI PM Jobs | 2025 Job Search Masterclass

• The AI-Powered Product Manager

• 5 Key Skills for AI Product Managers

• Decoding AI Product Hiring

• The AI Product Manager Playbook: Skills, Hiring, and Career Growth

My Perspectives:

Coming soon

1. How to Demonstrate AI Product Thinking in Interviews

2. Navigating Technical Questions Without a CS Background

3. Case Study Approaches for AI Product Management Interviews

4. How to Position Your Traditional PM Experience for AI Roles

+ More

---

Check all future posts here: Niche Skills: AI Product Management.

This space will evolve as I continue to learn and adapt to the ever-changing world of AI product management.

This page isn’t about overwhelming you with information—it’s about empowering you with clarity and confidence.

AI is changing daily—and so will this page. Some themes may expand; others may evolve entirely as new challenges emerge and new solutions are discovered.

My goal is simple: to share what I’ve learned so that we can all stay future-proof together.