White Paper

Defensible Validation of Relativity aiR for Review: Metrics, Methods, and Considerations

Legal teams face mounting pressure to review ever-larger, more complex data sets with both speed and defensibility. Generative AI offers a powerful way forward—but only if you can trust that its results are accurate and defensible.

Relativity aiR for Review provides that confidence with its built-in validation workflow embedded directly in Review Center. By statistically validating prompt criteria and performance upfront, you can measure precision, recall, and elusion before full review—reducing risk, preventing rework, and ensuring defensible results at scale.

This white paper shows how aiR for Review makes validation proactive, strengthens human-AI collaboration, and builds trust through measurable evidence. The result: a review process that’s faster, smarter, and aligned with industry standards.

Download the paper to learn how you can easily validate performance, avoid costly missteps, and bring defensible generative AI into your review workflow from here on out.