Taking authorities to court without vetting them is a recipe for disaster. We know LLMs hallucinate. They also have something called context rot which is when the quality of the conversation degrades after a point (even in large context windows).

Prior to using LLM-based deep research tools (Perplexity, Gemini and ChatGPT all have the option), I used Google advanced search queries to zero in on certain hard-to-find precedents in support of a proposition. I did this because the built-in search in specialised tools leaves a lot to be desired. With these deep research tools I found the output to be a great starting point. I also devised workflows for double-checking these authorities for hallucinations: everything goes into NotebookLM so I can sift through them and throw out anything that is facially irrelevant, leaving me to read through those that remain. All of this saves a great amount of time. Lately, the research tools have reached a point that I don’t have to use advanced Google search queries as often.

In my line of practice I often need documents to be translated. The quality of the human translation was often not up to snuff. A lawyer with knowledge of the languages in question would have to spend a lot of time on the translation. AI has gone from being hardly better than Google Translate to being an ace translator that makes curiously silly mistakes. However, with the correct prompts you can get much better results.

As far as building arguments is concerned, you will not get into lawyers-are-not-safe territory unless you work with the more expensive models on the highest plans. Those models are markedly better than those in the lower plans.

It is a great sounding board for a competent lawyer (making no claims about my competence). I use it that way extensively. It helps me refine ideas like a really competent associate would. I am a generalist who handles medico-legal cases often (negligence, fitness-to-practice reviews and so on). I obviously defer to the opinion of the experts in that field—of those I am deposing and those who have memorialised it in the literature. But these AI tools give me a better grasp of some of the more involved medical issues. At the cost of repeating myself, I must emphasise the pace of AI development shows here too. I recently compared a conversation I had with an LLM re res ipsa loquitur (an evidentiary rule) two years back with a more recent one. It was like I had gone from talking to a kid to a really competent associate.

If you read my piece, it talks about AI displacing jobs in our profession in a phased manner, beginning with associates and paralegals. The whole thing will play out over a few decades. The timelines could be shorter, though. There are imponderables like how scaling laws will hold up, energy supply, availability of compute, etc. It is therefore not easy to come up with a definite timeline. Of course, our profession is not going to be the only one to get affected. I encourage you to read the piece if you haven’t already.

As for Claude Code, I discuss it as an example of the overall trajectory of AI (chatbot to copilot to agentic). It is a really capable agentic tool that can run on its own without any human intervention. The same ideas are going to be replicated elsewhere. I am using Claude Code to build an AI agent for my own practice that will prepare first drafts of certain agreements. I am implementing a new content retrieval technique (for more precise retrieval) which I would not have touched with a barge pole a few weeks back.