Masterclass: AI PM Product Sense

Godhuli Chatterjee

18yrs: Amazon, Google, Microsoft. MBA/MS

Master Tier-1 GenAI System Design: From Google/Amazon Trenches to Passing Loops

The market is flooded with AI hype, but if you're interviewing for top-tier roles or leading an ambiguous charter, surface-level buzzwords won't save you.

Whether you are preparing for a grueling FAANG loop, upleveling your current job performance, or pivoting from a traditional vertical (program management, BI, marketing, project management) into an AI product operator, this workshop delivers the raw, un-hyped reality of how AI systems are built and broken.

Based on lived, Tier-1 engineering scenarios from Google and Amazon, you will master:

Production Realities: When to use RAG vs. Fine-Tuning, the truth about mitigating hallucinations, and exactly what evals to deploy when.
The Big Tech Playbook: What Tier-1 companies actually use AI for behind the scenes, and what they don't.
The Interview Blueprint: Learn exactly what bar-raisers ask in GenAI loops, how to scope complex system design prompts, and how to defend your architectural choices under cross-examination.

Stop guessing what elite tech companies want. Skip the textbook theory, learn the fundamentals, and command your next career growth.

What you’ll learn

Learn product sense basics, ML-to-AI concepts, and RAG. This Google/Amazon blueprint transforms you into an elite, loop-ready AI operator.

With or without AI, companies still need to build product that optimizes cost and improves adoption. Learn how to speak Product Sense - AI

1. AWS Real-world use cased based on Titan 2. RAG, Training and Evals as a part of this use case

1. Interview glow-up for AI era 2. Tips and tricks for Tier1 loops

Workshop agenda

Product Sense Fundamentals
Goals, Customer Segmentation in the AI era, Pain points and Solutions
Titan based Sentiment Predictor - Use Case Study
Product Vision, AI KPIs, LLM Ops, Canary Testing and Launch
AI Interview Tips and Tricks
How AI is changing the landscape of interview expectations