September 2026
Intermediate
216 pages
5h 33m
English
Content preview from AI Evals in Practice
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Chapter 1 — Why Evals Are the Missing Discipline
You deployed your LLM feature. The dashboard is green. Latency is under 500ms, error rate is 0.1%, and token costs are within budget. A week later, a customer reports that the chatbot recommended a product that was discontinued six months ago. Another week, and a support agent notices that the summarizer is dropping the second paragraph of long tickets. A month in, someone realizes that the classifier has been labeling 15% of urgent tickets as “low priority” since a prompt change three sprints ago.
None of these failures triggered an alert. Latency was fine. The API returned 200 OK. Token counts were normal. The system was observably healthy and functionally broken.
This is the fundamental problem ...
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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.