Most teams treat evaluation as something that happens after shipping. A metric to check once the feature is live, a process to revisit when something breaks. This module was an argument against that instinct, and for good reason. You cannot improve what you cannot measure. And you cannot measure what you never defined.

Quality in LLM outputs is not a single score. It is correctness, relevance, Groundedness , safety, and tone, weighted differently for every product and every use case. Defining what good looks like is a product decision that has to happen before a single evaluation runs, not after the first user complaint arrives.

Offline evaluation tests your system against what you anticipated. Online evaluation learns from everything you did not. Monitoring keeps visibility alive while the product is running at scale. And reliability at scale means asking the hard questions about volume, cost, and edge cases before they become your users' problem, not yours to fix after the fact. The discipline this module was really asking for is simple: own the definition of quality before you ship, and own the practice of measuring it after. Not as a launch checkbox. As an ongoing responsibility.

Quick Exercise

Pick any AI feature you have recently used or are currently building. Write down answers to these three questions:

What would a perfect response look like for the most common user query this product handles?
What would an unacceptable response look like, and how would you know one if it appeared in production?
What is currently in place to catch that unacceptable response before it reaches a user?

If the third answer is unclear or uncomfortable, you have found exactly where to focus next.

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

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