AI BriefingHow Similarweb Evaluates Agent Reports Using LangSmith
Key point
Similarweb explains how it uses LangSmith to evaluate long-form agentic research reports through rubrics, faithfulness checks, traces, and more.
Details
Similarweb is leveraging LangSmith to precisely evaluate long-form agentic research reports. Going beyond simply checking outputs, it has built a variety of evaluation frameworks to verify agent performance.
The main evaluation methods are as follows:
- Rubrics-based evaluation: measures report quality according to defined criteria.
- Faithfulness checks: verifies whether the generated content is grounded in the underlying data.
- Traces analysis: tracks the agent's reasoning process to identify problem points.
- Baseline comparison: compares performance against existing models or previous versions.
Through this systematic approach, Similarweb can increase the reliability of the complex research reports generated by agents and continuously improve them.
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