How to Evaluate AI Search for the Agentic Era

Staff Data Scientist

The Core Challenge: What Makes Search Evaluation Hard?

Al search and retrieval is now foundational to enterprise workflows. Yet, most teams don't have a clear evaluation framework, leading to hallucinations and poor performance. This technical guide allows your team to build more reliable Al Agents.

Key topics you’ll discover in this whitepaper:

How to build and use your "golden sets" for evaluating AI search: Learn to curate a definitive collection of queries to anchor your organization's consensus on quality.
How to deploy LLMs as impartial judges in evaluations: Learn how to score answer quality using LLMs, including sample prompts and code.
How to approach evals with statistical rigor: Leverage confidence intervals and variance decomposition to distinguish genuine performance improvements.

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.