I know someone who works in law and deals particularly with an area of US benefits and healthcare law. One of their workflows for lower-level employees at their firm involves taking in documents from healthcare plans and organizations, analyzing them for certain kinds of data, and then importing that data into an internal system they use to analyze and provide guidance on plans. The internal system can contain hundreds of documents for an individual client. All of the documents have the same information (roughly) but in totally diverse formats and styles. Once it's in the system, it's easy to compare and analyze across documents and the research process is much faster.
They recently bought a Claude subscription and began using Claude to do the initial read of the documents and output JSON they can import into their internal systems. The work still must be reviewed by an attorney - Claude is nowhere near making the kinds of judgments a lawyer would make about this content - but it has increased their throughput from 2-3 documents an hour to 8-10 documents an hour by killing the busy work.
LLMs have great advantages for this kind of work - but not for decision-making. I just don't see OpenAI ever admitting that.
(I've left some details intentionally vague because this is a very specific area of law and I don't want my friends to be identified without their consent.)
Doing similar-ish things with Claude, it's helpful to have something to ground it.
For instance, if you can say:
"Refer to the database schema in x.sql as your source of truth for the database structure we want to import int. Do not invent data, tables or columns that do not exist. Carefully match all output against this database schema and do not create output that doesn't exist if it does not match the schema, simply skip it."
You will end up with a far better result in my experience.
ivraatiems · · focus · HN ↗
They recently bought a Claude subscription and began using Claude to do the initial read of the documents and output JSON they can import into their internal systems. The work still must be reviewed by an attorney - Claude is nowhere near making the kinds of judgments a lawyer would make about this content - but it has increased their throughput from 2-3 documents an hour to 8-10 documents an hour by killing the busy work.
LLMs have great advantages for this kind of work - but not for decision-making. I just don't see OpenAI ever admitting that.
(I've left some details intentionally vague because this is a very specific area of law and I don't want my friends to be identified without their consent.)
refurb · · focus · HN ↗
You’ve accurately stated that AI isn’t as rigorous as a trained attorney. Doesn’t that mean that every single datapoint must be confirmed by a human?
How is that quicker than just using a human to read the content and make the call? Data entry savings?
juiceland · · focus · HN ↗
cromka · · focus · HN ↗
ferngodfather · · focus · HN ↗
For instance, if you can say:
"Refer to the database schema in x.sql as your source of truth for the database structure we want to import int. Do not invent data, tables or columns that do not exist. Carefully match all output against this database schema and do not create output that doesn't exist if it does not match the schema, simply skip it."
You will end up with a far better result in my experience.
Gotta treat it like a child.
chrisjj · · focus · HN ↗