Comment by ivraatiems
10 hours ago
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.)
I just realized how refreshing it is to read an honest take like "from 2-3 documents an hour to 8-10 documents an hour" instead of "it's doing the work of a month in 5 minutes!!!!1".
Agreed. However:
That's a factor of 3 to 5 improvement reported on here. If you keep stacking these kinds of improvements, you end up at month to minutes pretty quickly.
That doesn't take into account dependencies between tasks, where bottlenecks can happen. In any case, Amdahl's law applies regarding what kind of speedup you can get in such a case.
The AI can't make a reliable judgement, so now you have to hire 3x to 5x more to make those judgements if you have the documents coming in at 3x to 5x faster. Or you'll have one severely overworked lawyer. It's the same problem developers have with thousands of AI generated PRs. Just because you speed up one thing doesn't mean the whole system works faster, and yes a human really does need to be in the loop or you end up with even bigger problems, and lawsuits.
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10 women can't make a baby in a month
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> 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.
At my company we're also in the process of deploying a system that does exactly that. And what's interesting is that you absolutely do not need a frontier model for that, a small VLM (vision-language model), with optionally a little bit of fine-tuning, gives you the same output quality at a fraction of the latency and cost.
I'm wondering if the bottom is going to fall out of frontier models when people start to realize this. Sure, as an experienced programmer you can tell the difference between a compact free model and the ones that are 100x bigger and cost billions to train, but 90% of busy work is dead simple: turn a PDF into json fields, or do the inverse and fill out a form. As the tools for this conversion become more widespread you can also imagine an economic shift toward efficient information brokers that make frontier models less necessary.
It's also not really clear if everyone is going to want a frontier model when the real implications sink in. Maybe we'll get sick of incomprehensible code optimizations and people wile tire of reading AI prose that feels ever-more-human. There might be a few use cases, but who is going to pay for this when providers start charging enough to be profitable.
I think this is the long-term reality of LLM tech and one of the objectives of engineers implementing solutions, finding the right fit for the job - the right model and cost to achieve high enough accuracy at the lowest price.
But that's phase two, phase one is finding localized problems to solve using LLMs and productize them. I'm reminded of cloud tech, where phase one was changing software to run in the cloud, and phase two was optimizing costs.
This is really where Gemini still excels. Honestly from my experience the others aren't as good.
Yes, we used Gemini before and it worked fine in terms of output quality.
But between 10 and 20 seconds to analyze a document with only a bunch of pages just isn't good UX.
Happy to see measured results that isn't breathless glazing, and I suspect this is the kind of thing happening everywhere all at once. The other thing to consider is "burnout", in quotes because I mean that the work you describe sounds kinda boring, so people would resent doing it after a while and productivity would slow right down. Automation is the solution to tedium / toil, has been long before AI.
LLMs are still absolutely horrid at analyzing PDFs so the results they are getting must be chock full of errors..
Are they? I've had excellent success. The confusing part of this is that there are two types of PDF. The first is a "normal" digital PDF. The second is a scanned PDF. The first can essentially be read like a document. LLMs have no issues with this. It's the second kind of PDF where the constraint becomes the vision capability, and this is very impressive with Astra. I've had no issues with either. I imagine there could be issues with unusually dense and/or misaligned text on scanned PDFs, but I have not tested this.
The bottom line, though, is that PDF OCR is usually regarded as a solved problem. LLMs won't usually do the recognition itself. It will farm it out to established tools which are very good.
Well, I would argue about the first part. Even if they contain "native" text that can be extracted, in most cases their order will be messed up and it is often crucial for correct parsing.
So in many cases the visual way is the only one that works correctly, the textual one is just a shortcut that may be walkable in certain cases.
Which models are you using.
How do these use cases stack up (for real) in legal AI tools like Legora and Harvey?
Disclaimer: I used to work in legaltech, but not those two companies.
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?
Yes, remember that these are effectively random PDFs in various different designs and formats, some of them not editable or even OCR'd.
It took a human attorney 20-30 minutes on average to manually copy-paste data from these PDFs into a spreadsheet (while also fixing any errors they found in the document and re-checking for quality).
Now, the AI copies everything into the spreadsheet in a small amount of time, and then the human reviews it. It takes maybe ~5-7 minutes to scroll to the appropriate pages in the document, read the lines vs the spreadsheet, and make corrections. So you've gone from 2-3 items an hour to ~8-10 items an hour.
Maybe you could pay someone to develop an OCR/ML application that could do this. But that project would never be profitable, even with the time savings. At the cost of a couple Claude subscriptions, it makes sense.
Does the human find enough bugs that they stay on guard, or just rubber stamp everything without really looking at it? It’s hard to stay vigilant when stuff looks plausible.
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Don't forget that humans have a not insignificant error rate when copy/pasting or copy/typing data.
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I'm doing some public court records processing for bankruptcy cases (interested mostly to seek out corruption in big national cases), and yes, the "variousness" of random PDFs is exactly the issue. Trying to get the cost for a whole case down to a minimum.
Sample is around 300 court dates, shy under 1k files.
At best I'm building a claude skills file.
> Maybe you could pay someone to develop an OCR/ML application that could do this. But that project would never be profitable, even with the time savings. At the cost of a couple Claude subscriptions, it makes sense.
A better use of these Claude subscription would be to develop the app (which it can pretty much do at that point) and you could iterate to make the workflow even more efficient than your current one.
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You don’t need a trained attorney to schematize data. The LLMs are used to make the data easier to understand and manipulate.
They'll also hallucinate and change meaning in the process of extraction and "schematization"
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Reviewing something takes less time than producing it.
I...don't think that is universally true.
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I'm a lawyer myself.
I see problems in LLMs doing research, making drafts, etc.
I see 3 kinds of potential users.
1. Non legal background people trying to avoid going to a lawyer. They should definitely NOT use an LLMs.
2. Fresh out of law school graduates.
They think they can do fast work, draft quick replies, do fast case research, enh. They should go the traditional route of interning, learning the "art", they probably won't need it because they will learn "how the law works"
3. Established attorneys. They usually have templates made for most things, over their career, they have established routines of making things fast, maybe it can help them but to say replacing good chunk of work or offloading to LLMs isn't probably going to go well.
There is a reason why interns are made to do grunt work. The "chain of command" is built so they learn on basic stuff, learn to make decisions, etc. Without these basic decisions under their belt, an intern can't take bigger decisions later on. They just won't know....
>Non legal background people trying to avoid going to a lawyer. They should definitely NOT use an LLMs.
I used LLMs to replace a lawyer this year to great effect. I successfully advised myself as to the right strategy and drafted an immigration petition in the correct language. Granted it wasn't 100% LLMs (I also supplemented it with "traditional" online research and wrote the letter myself using the LLM version as rough guidance), but still, I certainly would've gone to a real lawyer in 2024 for this stuff but didn't.
Professional advisory work can be divided into two types:
1. Once you know the rules of the game, the next move is immediately obvious.
2. Knowing the rules is not enough to know the next move -- that requires judgement and experience.
LLMs in the hands of someone smart and savvy can easily replace the first type of work.
If it is at all possible to replace a lawyer with an LLM, it should always be the preferred choice. Using a lawyer is not a zero-sum game, using an LLM is.
Lawyers nearly universally tend to: convince clients they need a lawyer; promote extreme views; charge legal fees (that ultimately result in financial damages to the client). There are no winners in this game, except for lawyers, who are having a lovely time. Even if you won the case, you've lost, because the pie got smaller due to lawyer fees.
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You sound much like software engineers sounded when the first coding agents dropped.
"You can't trust the output, it doesn't understand bigger systems"
"Its an art, you need to learn the ropes of it to truly write good code"
Its a very dangerous line of thinking. Software engineering will never be the same, as writing code has basically vanished from the daily workflow. Not for every specialized usecase, but for many.
> Non legal background people trying to avoid going to a lawyer. They should definitely NOT use an LLMs.
In my experience, it is incredibly hard, practically impossible, to find a lawyer who will even talk to you. I think this is a valid use case to get at least some understanding what the next steps might be and if it even makes sense to seek legal representation.
Agree. Now imagine the same thing in less developed and/or less well off countries.
There was a post here a few weeks ago about a UK court being inundated with some kind of claims (unfair dismissal?). To submit a claim you do not need a lawyer, but the process is (intentionally?) vague and very complicated, so it takes a lot of effort to figure it out yourself. But AI makes that kind of stuff easy.
AI is not going to replace layers billing big corporate clients millions a month, but it will replace things that are fairly standardized processes, that don't require a lawyer, but today are just too complicated for regular people.
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> In my experience, it is incredibly hard, practically impossible, to find a lawyer who will even talk to you.
Really? I find them quite talkative. But I'm not sure what kinds of questions you are pestering them with?
In any case, the whole discussion reminds me of people using LLM to replace medical professionals.
For both: unless you have a doctor or a lawyer always on standby, you have to make a decision on whether to even go and seek out one of these professionals. By definition, you make that decisions without professional help. I think LLMs can help you there with the initial research to decide whether it's even worth it to contact the professionals.
Might be true today, but I still feel pareto principle applies. These points also sound like gatekeeping a bit and I hope future AI versions prove that law is also essentially something that can be made as programmable as possible for most common answers if the underlying principles and constitution are organised such.
If there is no precedence for X happening when Y , Z, P, Q, R are special combinations , the judge decides and sets precedence. But if another judge interprets it differently with similar set of circumstances it is not considered incorrect I presume especially if in different jurisdiction unless overridden by a higher court. This is my layman understanding but I think this design is wrong and essentially where human subjectivity controls the system.
Subjectivity is not totally avoidable in law but the goal of the legal system should be reduce it as much as possible along with ambiguity.
Your understanding is correct, but where your understanding goes astray is thinking that that's a bug. The subjectivity is a feature. It is not possible to specify what should legally happen for all possible circumstances the entirety of humankind faces. Like, literally not possible. We humans can't even create rigorous specifications for what our programs should do that keep up with their evolving requirements, and you want to make life-ruining decisions set in stone based on a rigorous set of predetermined rules about all possible circumstances the entirety of humanity could face any time in the future in an evolving world?
Law is intentionally not code. The world is not something you can program.
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> There is a reason why interns are made to do grunt work. The "chain of command" is built so they learn on basic stuff, learn to make decisions, etc. Without these basic decisions under their belt, an intern can't take bigger decisions later on. They just won't know....
That might be true, but that doesn't mean you benefit from training up juniors.
I do think non legal background folks can use LLMs today to sense check legal ideas, like for instance, "what are my legal rights in this situation?"
These situations usually are not ones that an individual can justify the time or money to contact an actual lawyer, but then if they do decide to contact one they will come in with better questions and more of a sense of what they are expecting.
This is similar to medical. Should you use LLM to diagnose yourself, treat yourself with prescription drugs you buy from shady gray market online sellers? No. But you can use it very well to know when it's time to go to the doctor and what to ask.
Yes. Unless you have a lawyer or doctor on standby, you have to make the decision whether to contact them. And by definition you have to make that choice without professional help.
LLMs can help with that. I don't think they are worse at this than me trying to figure this out all by myself.
You can replace "laywer" with "software engineer" in your post and it holds equally true. I think this also applies to other factions.
>I see 3 kinds of potential users.
3 kinds of users that don't scare the shit out of you.
My buddy works with lots of folks that have gone all in on Harvey. There are other kids of users.
There are 3x as many lawyers per capita in the US as we had in the 1970s. Credit agreements that used to be 50 pages are now 500. This increase in volume has not lead to any increase in actual value. The legal profession is a metastatic cancer on our society. Un-fuck this situation and maybe I'll think about taking your advice of not using a LLM.
Another thing that happened in 1971! https://wtfhappenedin1971.com
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Once again, LLMs comes to solve a problem that should not be there in the first place.
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They'd be better off outsourcing to lawyers in another country.
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My dog can review documents at an even faster rate.
You havent given any proofs or even comments that the work is the same level of quality or accuracy.
I'm thinking back to some of my interactions with corporate lawyers and you know what? My bar (<- pun) for trusting Astra more than a lawyer is pretty low.
Visit a developing or corrupt country and you will realise the LLMs of today analyse better than the judges from purely precedence and literature review POV
The proof would be that the attorney who did the work before, and who still reviews all ingested data, says it is.
Exactly.
The statements in the post are opinions, there is no actual PROOF they are true, and thats my point.
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You should free your dog then because a creature that intelligent should not be anyone's pet.
And free the biglaw junior associates while we're at it!
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