What Working on a 200,000-Line Privilege Log Taught Me About Generative AI

Summary: Generative AI’s real strength lies in its adaptability—and in this blog, we share how it delivered fast, regulator-ready privilege log descriptions without sacrificing nuance or consistency. From custom prompts to flexible workflows, it’s a look at how GenAI is reshaping legal work under pressure.

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We’ve been working with large language models (LLMs) since 2019, long before GenAI became a buzzword. Back then, we focused on predictive LLMs for things like document classification. But with the rise of generative models, our toolkit, and mindset, has evolved. And one thing that’s become clear is that not all AI is created equal.

Predictive AI is still unmatched in areas like classification. But the strength we’re seeing in generative AI lies in its adaptability. The ability to tailor outputs to nuanced requirements, without losing the consistency that makes automation worth it, is where GenAI is starting to show real ROI.

I saw this firsthand when we were brought into a matter involving a privilege log that was, quite frankly, overwhelming in size and urgency. The client had to produce a log with more than 200,000 entries, and fast. But speed wasn’t the only issue. The bigger concern was that the descriptions couldn’t look like they came from a pick list. Regulators were scrutinizing every detail, and they’d already made it clear: Generic or overly broad privilege descriptions wouldn't pass muster.

Outside counsel was in a bind. They needed precision and consistency without tripping red flags. Manually drafting the log simply wasn’t an option because any extension in the timeline could have put the entire deal at risk.

That’s where generative AI came in, not as a plug-and-play tool, but as a flexible solution. My colleagues and I started by tailoring a prompt specific to the dataset and the need for unique-yet-consistent descriptions, grounded in the language and context of the matter.

We had GenAI analyze a sample set of privileged documents and sent the initial outputs to outside counsel. Their feedback helped us iterate quickly and refine the prompt until we had a version they felt confident in.

The refined prompt was then used to run AI analysis across the entire privileged population, with results fed into a workflow to finalize the privilege log.

The final deliverable was a 200,000-line privilege log with unique, defensible descriptions—none of which were manually written by outside counsel.

This was an edge case for us, both in scale and emphasis on unique log lines. But it underscored something that’s becoming increasingly true in eDiscovery: GenAI gives us a way to adapt at scale without having to reinvent our systems. And when time, scrutiny, and quality are all at a premium, that kind of flexibility exactly what legal teams need to combat today’s datasets.

Curious how this approach scales across other privilege review challenges? Explore how Lighthouse AI for Privilege is helping legal teams move faster—with defensibility and precision built in.