September 2026
Intermediate
216 pages
5h 33m
English
Content preview from AI Evals in Practice
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Chapter 2 — Anatomy of an Eval: Datasets, Scorers, and Runners
In the previous chapter, we wrote the simplest possible eval: a list of tuples, a comparison function, and a for loop. It worked, but it was brittle. The dataset was hardcoded. The scorer was a single function with no interface contract. The runner had no error handling, no timing, no aggregation. To build a system that scales from 10 test cases to 10,000, that supports multiple scorers simultaneously, and that produces actionable reports, we need proper abstractions.
In this chapter, we will build the three pillars of evalkit: the Dataset loader, the Scorer protocol, and the Runner class. These are the composable building blocks that every evaluation in this book will use. By the ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.