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Astra for Law

589 points · 689 comments · vertigoruntime

  1. ivraatiems · · focus · HN ↗
    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.)

    1. refurb · · focus · HN ↗
      I’m curious how this increased throughput happens.

      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?

      1. juiceland · · focus · HN ↗
        You don’t need a trained attorney to schematize data. The LLMs are used to make the data easier to understand and manipulate.
        1. cromka · · focus · HN ↗
          They'll also hallucinate and change meaning in the process of extraction and "schematization"
          1. margalabargala · · focus · HN ↗
            Not necessarily. Depends how you use it.

            "Write a python script that breaks down this PDF by X feature" would not hallucinate anything in the PDF. Certainly you could trivially double check that all text in the extracted JSON was in the text layer of the PDF.

            1. chrisjj · · focus · HN ↗
              That's a recipe for disaster in my experience. I tried it (with Claude) on a simple tabular bank statement PDF, and it transposed two amounts, placinh each against the other's description. And the bot assured me the result was cotrect. The chance of a human checker catching such corruption is low.
              1. margalabargala · · focus · HN ↗
                Interesting. Did that PDF have a text layer or did you ask Claude to OCR it? If the latter I'm not surprised at all.
                1. chrisjj · · focus · HN ↗
                  [delayed]
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