Earlier quoted context omitted.
IME so far (as both a lawyer and a software engineer), LLM error rates when drafting code and legal documents are reasonably comparable, but it's more problematic in the legal context because legal documents do not benefit from many of the structural safeguards available for code. For legal documents, there are no automated tests, no static typing, no test environments, no logging/observability instrumentation, no sa…
There is also one thing I would like to add, and you can correct me if you disagree: coding benefits much more from thorough planning. Now, I exclusively work by first writing a plan that has well-defined steps and goals, which can of course change over time. It seems to me like it would be more difficult to achieve with legal documents and, in my experience at least, writing a concrete plan has been the decisive fac…
I've also adopted an AI coding workflow that involves a lot of planning, although I actually write very little of the plan myself anymore. I have a chain of slash commands like this: create-issue -> plan-issue -> build-plan -> pr-into-dev. I write a relatively brief description of what I want accomplished to create the issue, and then the agent fleshes out my description with more detailed requirements and acceptance criteria. I review the issue description, and the LLM often identifies open questions I failed to consider, so I revise as necessary and then the agent posts the description to the GH issue. I have planning separated because I often create issues quickly when something occurs to me and then circle back at a later date to implement, and want the agent to create the concrete implementation plan with an up-to-date snapshot of the code in context. Then I review that again, adjusting as necessary, and then the agent posts the result as a comment on the original issue.
Like you, I've found this detailed planning makes for a very robust coding agent (again, also in combination with the aforementioned best practices, especially requiring 100% test coverage because forcing it to exercise every line of code avoids hallucinated dummy tests that assert on nothing). Interestingly in comparison to legal writing, I also rely on the agent to decompose complex tasks into separate issues or subissues as appropriate, which is something that is never necessary for legal analysis because pretty much every every legal analysis can be one-shotted.
For legal writing, my workflow is nowhere near as structured as that. For context, I have only ever used LLMs for drafting what are effectively emails to clients or memoranda of law for clients that are a step up in complexity and formality from an email. So not something that will be filed with a court necessarily but very much in the same format and style as a formal motion that would be submitted to a court on behalf of a client. And never a contract, will, or judicial opinion, nor a communication with a counterparty like a demand letter or C&D. So YMMV for other types of legal writing.
That said, I typically start drafting a memo by conversing casually with an agent to explore the general boundaries of an issue I am evaluating, by identifying relevant sources of law, potentially related issues, and the analytical process I need to follow (i.e., what issues to evaluate and what order to evaluate them in, more or the less the analytical "algorithm"). Once I have a good sense of that algorithm, I put together a high level outline and then ask the agent to draft a detailed memo around that outline. Or at least that's what I used to do before the last few months, since when the models have matured to the point where I increasingly just ask the agent to write the outline based on the conversation we had, then review that, then ask it to write the memo based on the outline.
As I have been writing this, it occurs to me that actually I am following almost the exact same process for writing code and for writing legal memos, and should probably distill the legal writing process into a similarly well-structured set of chained skills/slash commands. In both domains, I describe an issue at a high level, get the LLM to fill in some of the broad outline level details, review that, then get the LLM to implement the complete final product. (Also perhaps worth noting while I do occasionally conduct general high level research by talking to a frontier lab LLM, I have always used locally hosted OS/OW models for drafting memos where I need to provide concrete, specific factual information about clients to the LLM, to avoid attorney-client privilege issues, so the quality has lagged behind the frontier models, which is part of why I haven't developed this workflow into as structured of an approach as I have for coding).
In both coding and legal contexts, I think that this planning or outlining step is critical not (or not just) because it forces the agent to create a higher quality product, but because it forces me to review what I am asking the agent to do at a sufficiently detailed level that I can catch errors before they crop up in the implementation. A lot of the time, the errors that occur if I skip this step aren't because the LLM has made any clear mistake, but because I failed to specify some aspect of the task and the LLM is forced to guess at what I really intended, which is where agents often struggle.
So I guess I would tentatively suggest that legal writing does in fact benefit from thorough planning, though it is hard for me to quantify whether those benefits are greater or less than the comparable benefits for code.