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Ship Real Agents: Hands-On Evals for Agentic Applications — Laurie Voss, Arize
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TL;DR · AI Summary
This article introduces how to evaluate AI agent systems, including setting up tracing, analyzing data, writing different types of evaluation methods, and meta-evaluation.
Key Takeaways
- Need to capture raw data through tracing to run evaluations
- Three types of evaluations can be used: code evaluation, LLM evaluation, and cus
- Meta-evaluation is used to verify the accuracy of evaluators' judgments
Outline
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Introduce speaker Laurie Voss and her experience in AI evaluation.
Explain why evaluation is more complex for AI agent systems than simple LLM calls.
Explain how to capture raw data needed for evaluations through tracing.
Introduce three evaluation methods: code evaluation, LLM evaluation, and custom evaluation.
Describe how to verify the accuracy of evaluators' judgments.
Mindmap
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查看大纲文本（无障碍 / 无 JS 友好）
- AI代理系统评估
- 评估重要性
- 比简单LLM调用更复杂
- 追踪设置
- 捕获原始数据
- 评估类型
- 代码评估
- LLM评估
- 自定义评估
- 元评估
- 验证评估者判断
Highlights
Key sentences worth saving and sharing.
Need to capture raw data through tracing to run evaluations
Three types of evaluations can be used: code evaluation, LLM evaluation, and custom evaluation
Meta-evaluation is used to verify the accuracy of evaluators' judgments
#AI Evaluation#Agent Systems#LLM