Hi everyone! I'm still pretty new to data engineering, mostly working on moving data from A to B, but my company is starting to explore using an LLM to help draft standard contract clauses. It's super interesting, but my manager just asked me a question that's left me a bit stuck.
He wants to know if we can build an automated evaluation to flag generated clauses for potential "legal risk." I get the concept of evaluating for correctness or style, but legal risk feels... fuzzy and super high-stakes. I'm worried about oversimplifying it.
I've been looking at frameworks like LangChain's evaluators or the HELM benchmarks, but they seem more geared towards factuality or toxicity. Has anyone here actually tried to build an eval for something like this? What would you even measure?
My initial, probably naive, thoughts were:
- Checking generated text against a library of known problematic phrases (like overly broad indemnity clauses).
- Using a second LLM call to act as a "reviewer" and score risk on a scale, but that seems circular and expensive.
- Looking for missing required elements (like termination notices) based on a contract type template.
Is this a fool's errand? Should we just be using the LLM as a first draft tool and leaving 100% of the risk assessment to human lawyers? I'd love to hear if anyone has tackled something similar, especially what your scoring rubric or evaluation dataset looked like. Even pointing me to relevant research papers would be a huge help!
Yeah, that "second LLM as a reviewer" idea seems like it could get weird and expensive fast, like an echo chamber. I've seen people try similar things for checking marketing copy, and the costs add up quick.
Your other thought about checking against known bad phrases seems more practical to start with. Maybe you could even build a simple checklist from your company's past redlines? Like, if legal always flags certain liability caps, the system could scan for those.
But I'm with you on the fuzzy, high-stakes feeling. Doesn't this *have* to go to a human lawyer for a final look? Would the eval just be a first-pass filter?
Your "fool's errand" question is the right one. This isn't an eval problem, it's a process problem.
Building an automated legal risk scorer is a massive liability. The echo chamber point is valid, but the bigger issue is creating a system your company might rely on. If your "eval" misses a critical flaw, who's responsible? Your manager or the bot you built?
Start with your third idea: flagging missing required elements against a strict template. That's a straightforward validation step, not a risk assessment. Leave the actual legal judgment to the humans, where it belongs.
Beep boop. Show me the data.
I completely agree about the liability being the real core of the issue here. The "process problem" framing is spot on. Even if you built a 99% accurate risk scorer, you'd have created a new, uninsurable point of failure in your legal workflow.
That said, I think the template validation approach is the only safe starting line. You can build some incredibly useful checks without ever judging the clause content itself. For instance, a system could:
* Verify that any defined term (like "Confidential Information") actually appears in the definitions section.
* Confirm that a clause referencing a specific law includes the current, correct statute citation pulled from a locked database.
It's all about automating the clerical, not the cognitive. Makes the human review faster and less error-prone, but the buck still clearly stops with them.
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