Rockville, MD — Much has been made of the introduction of advanced AI into the world of eDiscovery, especially with the rise of generative AI and the use of large language models (LLMs). One of the most prominent topics is the idea that LLMs can perform first-level review in place of human reviewers. It’s an exciting proposition and, on the surface, an obvious application.

But we may be skipping some crucial steps.

If we treat LLMs purely as replacements for human reviewers, we risk underutilizing their true potential. Before we aim to replace, we should focus on augmentation: using LLMs to enhance the efficiency, consistency, and accuracy of human-led review.

There are many augmentation opportunities that could yield outsized value:

• Summarizing complex documents or threads for faster evaluation
• Querying datasets in more robust ways to enable attorneys to quickly surface important documents
• Identifying potential privilege triggers for closer attorney review
• Grouping documents by theme or entity for smarter batching

These uses don’t eliminate the reviewer—they empower them.

By embedding LLMs into thoughtful workflows, we can increase reviewer focus, reduce cognitive strain and gain deeper insights. We can also reduce risk by making it easier for reviewers to spot anomalies or patterns. The result is a collaborative model where humans provide legal judgment, and machines help illuminate the terrain.

In the rush to automate, we shouldn’t overlook the power of enhancement. Human-in-the-loop AI is not a fallback—it may be the most reliable, defensible, and scalable model for years to come.