How We Evaluate AI Search for the Agentic Era

Evaluating AI search has become a critical challenge in the age of LLM-powered, agentic systems. Our latest whitepaper, “How We Evaluate aI Search for the Agentic Era,” provides a deep dive into the rigorous process of AI search evaluations (“evals”), going far beyond simple query testing.

This guide details why most ad hoc approaches to evaluations fail and lays out a four-phase methodology—problem definition, data collection, query execution, and robust statistical analysis—to ensure your AI search solution is truly optimized for your intended use case.

Download the full whitepaper now to elevate your approach to AI search and evaluations.

Key topics you’ll discover in this whitepaper:

• How to build and use golden sets for evaluating AI search, and why custom and benchmark datasets both matter for comprehensive evals.
• The use of LLMs as impartial judges in evaluations—validated against human graders—to score search result relevance and answer quality.
• Why statistical rigor, including confidence intervals and variance decomposition, is essential for trustworthy AI search evaluations.

Whether you’re comparing search providers, optimizing a Retrieval-Augmented Generation (RAG) pipeline, or building agentic systems, this whitepaper is your essential resource for running meaningful AI search evals and driving robust, reproducible evaluations.