OpenRouter publishes tutorial on building golden eval sets from production traffic
What happened
On September 30, 2026, OpenRouter published a tutorial on building a golden eval set from production traffic and re-testing across models to pick the next one. The five-step process: pull one to two weeks of production logs, dedupe and cluster, add a human-reviewed expected output for each input, run the current production model first and fix the scoring criteria, then commit to Git and wire into CI. Scale: about 10 items per exploratory question, 100 to 1,000 for a full regression set. A second tutorial the same day covers regression testing AI agents after changes to prompts, models, tool definitions or retrieval settings: rerun the locked case set each time, check tool calls and arguments with structured assertions, and constrain policy boundaries with hard invariants.
Written by AI from the coverage · updated 1 hour ago
Developments
- Sep 30 08:00 · 1 reportOpenRouter 教程：如何从生产流量构建 golden 评测集并跨模型复测AI HOT · Tips & opinions
- Sep 30 08:00 · 1 reportOpenRouter 教程：提示词或模型变更后如何对 AI Agent 做回归测试AI HOT · Tips & opinions
Coverage
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- AI HOT · Tips & opinionsOpenRouter 教程：提示词或模型变更后如何对 AI Agent 做回归测试
OpenRouter 发布教程，说明提示词、模型、工具定义或检索设置变更后如何对 AI Agent 做回归测试：每次重跑锁定的用例集，用结构化断言检查工具调用与参数，并用硬性不变量约束策略边界。
- AI HOT · Tips & opinionsOpenRouter 教程：如何从生产流量构建 golden 评测集并跨模型复测
OpenRouter 发布教程，讲解如何从生产流量构建 golden 评测集，并跨模型复测以挑选下一个模型。教程给出五步流程：抽取一两周生产日志、去重聚类、为每条输入补充经人工审核的期望输出、先跑一遍当前生产模型并修正评分标准、最后提交 Git 并接入 CI；规模上探索单个问题约 10 条，完整回归集 100 到 1000 条。
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