9 minutesLangSmith in 10 Minutes
You can navigate traces, projects and datasets in LangSmith and read off token cost, latency, error rate and per-span detail.
AdvancedAI for Business
How to Systematically Setup LLM Evals (Metrics, Unit Tests, LLM-as-a-Judge)
Dave Ebbelaar. A working AI engineer walking through his actual eval ladder — assert-style unit tests, reference-free metrics, LLM-as-judge alignment with humans, and the analyze/measure/improve loop. The structure is the closest match on video to the article's argument that evals are a regression-catching system, not a leaderboard.
AI Expert note
Some tool choices will age, but the ladder is sound: deterministic checks first, then model-graded checks validated against humans. Do not skip calibration just because an LLM judge is easy to add.
What you should get from this
Design an eval ladder that catches regressions before prompt or model changes reach users.
Watch or know first
Experience shipping or maintaining an AI workflow with known failure examples.
Last reviewed: May 18, 2026
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