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"Tuning Generative AI-Based Systems To Enhance Document Review"

Legal teams face a growing need for defensible AI systems that can reduce human review time, and deliver reliable results at the same time. This case study details an in-depth, statistically grounded evaluation of a GenAI-powered review platform--revealing how targeted prompt engineering and thoughtful mode selection can improve accuracy and minimize borderline files.

As generative AI gains traction in legal workflows, legal professionals are under pressure to evaluate which systems actually deliver on their promises of precision, defensibility, and efficiency. However, with complex architecture and varying performance across review types, making the right choice is far from straightforward.

This case study explores the performance of a leading GenAI-based document review system using real-world data and statistically rigorous testing. The team evaluated three distinct review modes—Relevance, Issues, and Relevance + Issues—against a dataset of 26,000 documents, applying structured benchmarking and advanced prompt engineering to identify the most effective configurations.

The results highlight how deliberate mode selection and iterative prompt tuning can reduce manual review requirements, minimize borderline errors, and align system outputs with legal team objectives. Whether you're concerned with production responsiveness, complex issue categorization, or reducing downstream QA burden, this case study helps you evaluate where AI fits in your review process—and how to use it more effectively.

Download the case study to learn:

• How each GenAI review mode performed across key metrics like recall, precision, and F-score
• When to apply Issues Review vs. Relevance + Issues for best results
• How prompt engineering reduces borderline classifications and boosts reliability
• Why inter-run variability matters for GenAI review reliability
• What to expect from real-world application vs. training datasets

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