by Ravin Thambapillai
Enterprises are starting to use LLMs, but we’re still in the early days.
We’re all familiar with the basic use cases: search over documents, customer support, and so on. But the harder problems come with regulated enterprises dealing with large amounts of sensitive data — where you have to deal with thorny technical issues like data integration, prompt injections, permissions, and auditability.
This post lays out a case study of how LLMs can be used in an AML (anti money-laundering) context, and some of the gotchas along the way.
This is the first in a series of case studies of how businesses are unlocking these more complex LLM-based workflows with regulated, or sensitive, data. Hopefully it can inspire you too!
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