The need for actual lawyers will persist I think from my own experience. I attempted drafting a contract with some points myself using AI, but after several edits I wasn't sure if it was correct. Sending it to an actual lawyer ended up in so many corrections I couldn't imagine the first time. One big thing was the overly excessive protective clauses which didn't make sense for reality or conflicted with another.
Its just like code I suppose, if you can read and understand and validate, you can use it to scale and otherwise it could end up being a vibe effort.
LLMs are the first genuinely useful legal tech since the Internet. I'm pretty shocked, though, at the delta between how competent Claude is on code versus legal work. It's good for research and data organization, but terrible for drafting. I wonder if this is a structural problem with the lack of feedback loops. In law, there's no compiler to check for logical or continuity errors in your brief, and there's no unit tests to check for correctness or performance.
Even without that, I think it'll be extremely valuable to clients to allow them to answer simple questions without a lawyer, figure out the lay of the land so they can supervise their counsel, etc.
You should know - for coding they make terrible mistakes as well.
But programmers have this concept of a "code review" where another person looks at the code to look for problems.
We use this same technique with our LLMs. Most mistakes are caught by having a second LLM look at it. Doesn't even need to be a different model - just make sure it has a different context.
Programming has a long standing culture of accepting the code to be somewhat wrong, so we have various tests, linters, reviews and error handling. Also in programming there are many ways to do something and it's the end result that matters most.
Not so in other knowledge work. There's no test harness for a contract and error is non-recoverable. Likewise in finance. There are specific ways of doing things and these ways are many times set in regulations. LLMs can assist all day, sure. But replacing the human, in highly regulated, zero tolerance for error environment?
The highly regulated, zero tolerance for error environment is a huge problem because anyone doing these jobs is more like a small sample size LORA than a general model.
It doesn't matter how smart someone is, they need specialized training to be good at these jobs. Specialized training in the area the company specializes in.
There is a category error in all this that is hard to think about because of the normal discourse and ordinary language. We say people work in "finance" but no one works in just "finance". They work at a company that has a specialization within "finance", inside a hierarchy that has specialization on top of specialization.
What we really need is exactly what we don't have and aren't going to get. A type of LORA that generalizes the task specific intelligence needed from a very small sample size and that in practice makes so many less mistakes in a highly regulated, zero tolerance for error environment that it is irresponsible to not use the model.
I have worked in this type of environment for 3 years and I have made zero mistakes in 3 years. The people that make even a small number of mistakes get fired.
Any real automation in this area is going to be incredibly slow and piecemeal over a long period of time because even an amazing model would need a long time to prove itself against what the human standards for error rates are.
halamadrid · · focus · HN ↗
Its just like code I suppose, if you can read and understand and validate, you can use it to scale and otherwise it could end up being a vibe effort.
rayiner · · focus · HN ↗
Even without that, I think it'll be extremely valuable to clients to allow them to answer simple questions without a lawyer, figure out the lay of the land so they can supervise their counsel, etc.
qarl · · focus · HN ↗
But programmers have this concept of a "code review" where another person looks at the code to look for problems.
We use this same technique with our LLMs. Most mistakes are caught by having a second LLM look at it. Doesn't even need to be a different model - just make sure it has a different context.
flyinglizard · · focus · HN ↗
Not so in other knowledge work. There's no test harness for a contract and error is non-recoverable. Likewise in finance. There are specific ways of doing things and these ways are many times set in regulations. LLMs can assist all day, sure. But replacing the human, in highly regulated, zero tolerance for error environment?
telliosic · · focus · HN ↗
It doesn't matter how smart someone is, they need specialized training to be good at these jobs. Specialized training in the area the company specializes in.
There is a category error in all this that is hard to think about because of the normal discourse and ordinary language. We say people work in "finance" but no one works in just "finance". They work at a company that has a specialization within "finance", inside a hierarchy that has specialization on top of specialization.
What we really need is exactly what we don't have and aren't going to get. A type of LORA that generalizes the task specific intelligence needed from a very small sample size and that in practice makes so many less mistakes in a highly regulated, zero tolerance for error environment that it is irresponsible to not use the model.
I have worked in this type of environment for 3 years and I have made zero mistakes in 3 years. The people that make even a small number of mistakes get fired.
Any real automation in this area is going to be incredibly slow and piecemeal over a long period of time because even an amazing model would need a long time to prove itself against what the human standards for error rates are.