You can build the most thoughtfully designed LLM product in the world and still have no idea whether it is actually working. That is not a hypothetical risk. It is the default state for most teams that ship AI features without a structured evaluation practice.

The model responds. Users interact with it. But without a framework for measuring quality, the team is essentially guessing at whether what the product is doing is good, improving, or quietly getting worse.

Recent high-profile failures demonstrate why evaluation matters. CNET published finance articles riddled with AI-generated errors. Apple suspended its AI news summary feature after generating misleading headlines. Air Canada was held legally liable after its chatbot provided false refund information. None of these teams set out to ship broken products. They shipped without the frameworks to know their products were broken until users found out for them. This module is about building the discipline to never be in that position.

Foundations to Advanced Systems: LLMs for Product Managers

Module 1Introduction to Large Language Models in Product Context

Module 2The LLM System Architecture

Module 3Advanced LLM Workflows

Module 4Evaluation

Module 5LLM Risks

Module 6LLM Productions

Module 7Strategic Applications for Product Managers

Why This Module Matters