How to benchmark web search APIs on your own queries

The guide explains a comprehensive method for evaluating web search APIs on real production queries rather than relying on vendor-provided benchmark tables, which often do not reflect specific workloads. It outlines a process involving setting up a query set based on actual traffic, using a fixed harness, a large language model (LLM) judge for evaluation, and a scoring system that includes error bars. This approach emphasizes the importance of using one's own queries to assess performance, as public benchmarks may not align with unique query patterns and domain-specific needs. The guide provides practical steps for conducting these evaluations, including building a query set, running candidate APIs through a standardized loop, judging task success strictly based on correctness criteria, and calculating accuracy, latency, and cost per successful task. It stresses the importance of version control and regular re-evaluation to adapt to changes in API models or indexes, and it advises comparing candidates using a paired comparison method when results are close. The guide also highlights the need for transparency and consistency in reporting evaluation results and suggests treating the evaluation harness as a product to be maintained over time.

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