You do not need a PhD to interrogate AI claims. You need a method. This course teaches enterprise practitioners how to read papers, spot benchmark manipulation, design internal studies, and translate research findings into board-ready decisions.
You commission or review AI evaluations but did not design them yourself
You read vendor benchmark claims and cannot tell what is real
You manage a team that produces AI research and want to know if they did it right
You need to present AI evidence to a board or audit committee
You are a CTO, Chief AI Officer, CIO, or VP of Product in an AI-heavy org
This is NOT for you if...
You want to publish academic ML research (wrong course)
You are learning to train or fine-tune models (see Fine-Tuning LLMs)
You are a complete beginner with no AI exposure (start with What Is a Model)
You want to learn to run benchmarks in code (see Evaluating AI Models)
Prerequisites
ⓘ
You should be comfortable with what a language model does and what "training" means. No statistics background required. If you have taken Evaluating AI Models or Enterprise AI, you are well prepared. If not, Module 1 starts from scratch.
What you will be able to do
✓Read an AI paper and identify what the authors actually proved vs. what they implied
✓Spot the five most common benchmark manipulation techniques vendors use
✓Design a valid internal AI evaluation from scratch, including sample size and controls
✓Interrogate a team's AI research and know if they did it correctly
✓Translate a paper's findings into a board-ready decision brief
✓Run a vendor AI evaluation session and ask the questions that reveal real capability