How to Set Up and QC Your Own GenAI-Powered Document Review for In-House Teams
With document review being dramatically transformed by GenAI – and in-house teams increasingly under pressure to get more out of their technology, and to be experts on how their outside counsel is deploying AI – understanding how to set up and run quality-control on your own AI-powered document review is critical.
First-pass review, which can consume over half of a case’s ediscovery budget, can be done dramatically faster, with tools like DISCO Auto Review processing tens of thousands of documents per hour with precision and recall rates 10-20% higher than human reviewers.
Until recently, the catch was calibration. Getting an AI review aligned to your goals meant hand-rewriting tag descriptions, rerunning jobs, and comparing metrics — trial-and-error work that often called for a specialist. On an aggressive production deadline, that calibration window was enough to make teams think twice about AI-powered review at all.
Tag Tuner changes that. A new capability built into Auto Review, Tag Tuner acts as your built-in prompt engineer. You review the documents where Auto Review and your reviewers disagreed, explain in plain English why your reviewers were right, and Tag Tuner rewrites the tag description, tests it against the remaining disagreements, and shows you exactly how performance changed.
The practical result: running your own GenAI-powered review is now a realistic option for your team — no specialized AI training required. You keep the control with the folks who know the matter best. And the DISCO Managed Review team is still here when you’d rather we run it.
This guide gives you a practical framework for setting up, tuning, and validating a GenAI-powered document review with Auto Review, accelerated by Tag Tuner.
Ready to get started? Download the guide.
How to Set Up and QC Your Own GenAI-Powered Document Review for In-House Teams
With document review being dramatically transformed by GenAI – and in-house teams increasingly under pressure to get more out of their technology, and to be experts on how their outside counsel is deploying AI – understanding how to set up and run quality-control on your own AI-powered document review is critical.
First-pass review, which can consume over half of a case’s ediscovery budget, can be done dramatically faster, with tools like DISCO Auto Review processing tens of thousands of documents per hour with precision and recall rates 10-20% higher than human reviewers.
Until recently, the catch was calibration. Getting an AI review aligned to your goals meant hand-rewriting tag descriptions, rerunning jobs, and comparing metrics — trial-and-error work that often called for a specialist. On an aggressive production deadline, that calibration window was enough to make teams think twice about AI-powered review at all.
Tag Tuner changes that. A new capability built into Auto Review, Tag Tuner acts as your built-in prompt engineer. You review the documents where Auto Review and your reviewers disagreed, explain in plain English why your reviewers were right, and Tag Tuner rewrites the tag description, tests it against the remaining disagreements, and shows you exactly how performance changed.
The practical result: running your own GenAI-powered review is now a realistic option for your team — no specialized AI training required. You keep the control with the folks who know the matter best. And the DISCO Managed Review team is still here when you’d rather we run it.
This guide gives you a practical framework for setting up, tuning, and validating a GenAI-powered document review with Auto Review, accelerated by Tag Tuner.
Ready to get started? Download the guide.
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With document review being dramatically transformed by GenAI – and in-house teams increasingly under pressure to get more out of their technology, and to be experts on how their outside counsel is deploying AI – understanding how to set up and run quality-control on your own AI-powered document review is critical.
First-pass review, which can consume over half of a case’s ediscovery budget, can be done dramatically faster, with tools like DISCO Auto Review processing tens of thousands of documents per hour with precision and recall rates 10-20% higher than human reviewers.
Until recently, the catch was calibration. Getting an AI review aligned to your goals meant hand-rewriting tag descriptions, rerunning jobs, and comparing metrics — trial-and-error work that often called for a specialist. On an aggressive production deadline, that calibration window was enough to make teams think twice about AI-powered review at all.
Tag Tuner changes that. A new capability built into Auto Review, Tag Tuner acts as your built-in prompt engineer. You review the documents where Auto Review and your reviewers disagreed, explain in plain English why your reviewers were right, and Tag Tuner rewrites the tag description, tests it against the remaining disagreements, and shows you exactly how performance changed.
The practical result: running your own GenAI-powered review is now a realistic option for your team — no specialized AI training required. You keep the control with the folks who know the matter best. And the DISCO Managed Review team is still here when you’d rather we run it.
This guide gives you a practical framework for setting up, tuning, and validating a GenAI-powered document review with Auto Review, accelerated by Tag Tuner.
Ready to get started? Download the guide.
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