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.
Proof point: Braintrust, a third-party AI evaluation platform, tested You.com search against the model providers' built-in web search on 1,329 LiveNewsBench current-events questions across four models. You.com search raised accuracy for all four models, from +34.2 to +76.4 points over no search, and beat built-in search for GPT-5.6 Terra by 3.66 points while answering 3.40 seconds faster at $0.0051 less per question. Read the full search relevance eval.
