September 2026
Intermediate
216 pages
5h 33m
English
Content preview from AI Evals in Practice
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Chapter 12 — Tooling Landscape: DeepEval, promptfoo, Langfuse, Braintrust
You have built evalkit from scratch. You understand datasets, scorers, runners, regression detection, RAG metrics, agent trajectories, and cost optimization at a fundamental level. Now the question becomes: should you use your own implementation, or should you use one of the commercial and open-source tools available?
The answer, as with most engineering decisions, is “it depends.” This chapter provides a practical comparison of the four most relevant tools in the eval ecosystem, maps each to the evalkit concepts you already understand, and gives you a framework for making the build-vs-buy decision.
Where this fits in your eval maturity: most teams encounter the build-vs-buy ...
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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.