Why Your AI Search Evaluation Is Probably Wrong (And How to Fix It)

Ad-hoc testing of AI search systems often leads to poor, costly decisions because it fails to reflect production behavior or provide replicable results. A more rigorous five-step framework is proposed, starting with defining what a "good" result means for a specific use case and building a "golden test set" of queries with a clear grading rubric. The process involves running controlled, multi-trial comparisons across providers, using LLMs as automated judges, and validating their performance against human experts. Finally, it recommends using the Intraclass Correlation Coefficient (ICC) to measure evaluation stability, ensuring that observed performance differences are statistically significant and not just random noise.