Module 10 of 13 in the AI Track, a free, self-directed curriculum for building AI that moves revenue.
The idea
This is the unglamorous skill that separates the pros from the prompt-jockeys. Treat it as a moat. LLMs don’t give you the same answer twice, so you can’t ship a feature and assume it stays good. You need evals. A held-out set of cases, automated grading, often with one model judging another against a rubric, and regression tests on your prompts so a “small tweak” doesn’t quietly wreck the output.
What you want specifically: a way to measure hallucination, guardrails and fact-checking passes that formalize the skeptic checks people usually do by hand, and real monitoring in production. Logging, tracing, a cost dashboard. So you catch the drift before a client does.
Why it matters
This is what makes “no hallucinations by design” provable instead of a nice line. An eval that scores every message for whether it’s grounded in a real source before it sends is the strongest thing you can put in front of a buyer. It’s also the thing almost nobody bothers to build, which is exactly why it’s an edge.
Build this
An eval harness for whatever you built in Module 9 that scores every generated message for grounding before it’s allowed to send. Now the no-hallucination claim has a number behind it, not just your word.
How to work through it
- The eval chapters of Chip Huyen’s AI Engineering
- Eugene Yan’s writing on evals and LLM-as-judge
- Hamel Husain’s Your AI Product Needs Evals
- Build the grounding eval and a regression set
Resources
- AI Engineering by Chip Huyen
- Eugene Yan’s essays on evals and LLM-as-judge
- Hamel Husain’s Your AI Product Needs Evals