Listen...

If you're a Product Manager, you're probably feeling it. That quiet, creeping sense of anxiety.

You see the GenAI wave coming. Your engineers are talking a different language. Your boss is asking questions you can't answer. You're being asked to build roadmaps for technology you don't truly understand deep down.

You've spent your entire career mastering the Problem Space. But GenAI has changed the game. The winners of the next decade will be the ones who master the technology itself. If you can't confidently talk about transformers, RAG, and model feasibility... you're at a massive disadvantage.

You risk becoming irrelevant. But it doesn't have to be that way.

The Discovery That Changed Everything for 2,200+ Product Leaders

My name is Vikash Rungta. During my six years at Meta, I had a unique vantage point on this exact problem. As the Product Manager for Llama, focusing on the complex challenges of human alignment and safety, I saw brilliant, world-class PMs suddenly look lost when faced with GenAI's technical realities.

Through leading internal bootcamps and teaching externally, I've had the privilege of training over 2,200 product leaders—from 1,200 PMs & TPMs inside Meta to 1,000+ more at Stanford Continuing Studies and beyond. Across thousands of hours, I saw the same pattern emerge.

The problem wasn't a lack of talent. It was a gap in their mental model.

The "aha!" moment was realizing they didn't need to become coders. They needed a deep, intuitive understanding of the First Principles of how GenAI works. Once I gave them that framework, everything changed. They went from being timid observers to confident leaders. My mission is to give that same "Aha!" moment to you.

What You Will Master:

From Theory to Practical Leadership

- This transformation is possible because you will walk away with a specific set of skills. By the end of this course, you will be able to:

Explain the Core Technology

- Clearly explain the fundamental concepts, architectures, and training techniques of LLMs and foundational models to any audience—from engineers to executives.

Analyze Any AI Model

- Confidently analyze the capabilities, limitations, and potential biases of any state-of-the-art AI model and deploy strategies to mitigate their weaknesses.

Develop a Strategic Framework

- Build and execute an actionable plan for integrating GenAI into your products, accounting for crucial factors like cost-value tradeoffs and data privacy.

Evaluate Business Value

- Pinpoint high-potential use cases and accurately assess the business value and organizational impact of bringing a new AI feature to market.

Master Prompt Engineering

- Demonstrate professional-level skill in prompt engineering to effectively interact with and get the absolute most out of any generative AI model.

Here is Your Training Itinerary:

Introduction to Generative AI, Foundational Models and Path to AGI

Learning Objectives:

Define Generative AI (GenAI) and its relation to Artificial General Intelligence (AGI).

Understand the role of product managers in the GenAI era and the impact on product development.

Analyze case studies of successful GenAI products and their business impact.

Introduction to Machine Learning & Artificial General Intelligence 

Understand Machine learning & Artificial Intelligence fundamentals 

Build your first Machine Learning Model live (no coding) 

Use cases of ML Models, forming the fundamentals for GenAI

Anatomy of Foundational Models (FM’s) /LLMs

Understand the architecture (e.g Transformers) of Large Language Model - LLMs.

Explore the core capabilities and limitations of state-of-the-art LLMs

Learning to control LLMs  

Learn how to control LLM’s to output what you need. 

Learn how to configure LLM’s

Learn effective prompt engineering strategies and prompting techniques for interacting with LLMs.

Prompt as a product

Review use cases of products you can build using prompts

Learn to use various AI playgrounds, including openai playground, Google AI Studio etc. 

Vibe coding, learn to build product that can ship using vibe coding 

Build custom GPTs, that you can ship 

Hands on: Build multiple products that you can ship using prompts 

Building Agents 

What are AI agents

How are AI agents different from LLMs

What are tools 

When to build an agent vs LLM

Hands On: Build an agent 

MCP & A2A

What is Model Context Protocol & Why you need it 

What is Agent 2 Agent (from google) 

Building GenAI Systems

Identify potential use cases, opportunities, and business value of integrating GenAI into products.

Evaluate product and organizational considerations for implementing GenAI solutions.

Develop a strategic framework and actionable plan for integrating GenAI capabilities.

Overcoming Limitations of LLMs in product building

Identify common limitations of LLMs such as biases, robustness issues, hallucinations, and lack of grounding.

Understand strategies for mitigating and overcoming these limitations.

Analyze case studies to identify limitations and apply mitigation strategies.

Retrieval-Augmented Generation (RAG)

Learn the Retrieval-Augmented Generation (RAG) technique for integrating external data with LLMs.

Evaluate cost-value tradeoffs when implementing GenAI solutions

Finetuning 

Finetune your models 

LLM Safety

How to make your models Safe 

Input and output guardrails

Shipping GenAI Products 

How to move from POC to MVP to Deployment 

Exploring practical everyday applications and delving into the future of Generative AI.

Explore a wide range of use cases and applications of GenAI across various industries and domains.

Analyze real-world examples of successful GenAI implementations.

Identify lessons learned and best practices from these use cases and examples.