Home

One minute on video

Large language models have arrived in litigation practice. This site is a practical starting point for South African advocates and attorneys: how to set the tools up, how to use them on a matter, how to check what comes back, and where the professional duties sit. Why this site exists.

Lawyers are small language models

We were doing this long before the machines.

Think about how a junior learns. Years of reading judgments, heads, pleadings, affidavits. Nobody hands you the rules for how a court writes. You soak it up. After a while you can guess what the next paragraph of a judgment will say before you turn the page. You know how an answering affidavit “should” sound. Ask a senior what a court will do with a set of facts and you get a prediction, built from every similar case they have read.

That is, roughly, what a large language model does. It has read far more than any of us, and it predicts what comes next.

A junior advocate with a small stack of books and a language model with a huge stack; both predict what comes next.
Same trick, very different library.

The parallel goes further. Faced with a new set of facts, we relate it to what we have seen before. So does the model. And when the facts are truly new, both of us are guessing. One paper on language models and law makes exactly that point.

But the comparison breaks in places that matter.

We know when we are guessing. Or the good ones do. A decent lawyer says “I’d have to check that”. A language model’s answer sounds equally sure whether it knows or not. (Why that happens is its own post.)

We sign. Our name goes on the heads, and so do the consequences. The model has no name on the page and no reputation to lose.

We weigh values. Where a case turns on unsettled value judgments, the same paper suggests lawyers keep the edge. Patterns only take you so far when the question is what the law ought to be.

Venn diagram of what lawyers and language models share and where they differ.
The overlap is why it is useful. The edges are why you still check.

So I don’t think of Claude as a replacement brain. It is a very large, very fast language model working next to a small, slow, accountable one. The small one has to do the checking. That’s the job now.

Source: A View of How Language Models Will Transform Law (arXiv 2405.07826), on novel fact patterns and value judgments. The title of this note is my own analogy, not a term of art.

The blank chat box

Every AI tool greets you the same way: an empty box and a blinking cursor. It looks so simple. It’s also where most people get stuck. If your first few tries felt disappointing, it probably wasn’t you or the tool. It was the blank box.

Sketch: a puzzled stick figure looks at an empty chat window. The only thing in it is a message box with a blinking cursor, labelled: a blinking cursor, now what?
The blank box: an empty box and a blinking cursor.

How to fill in the blanks

The good news: getting better results isn’t hard. Tell the AI who you are, what you need, what material to work from and how you’d like the answer. Give it the documents instead of asking it to remember the law. Then check what comes back. It helps to picture a bright, eager junior on their first day. Quick and willing, but they need clear instructions and someone keeping an eye on their work.

Made-up example
Who you areYou are my junior. I am an advocate at the Johannesburg Bar, briefed in a contract claim.
What you needI need a two-page note on whether the claim has prescribed.
What to work fromUse only the documents in research/ and the particulars of claim in pleadings/. Nothing from memory.
How you want itAnswer first, then the reasons, with a document and paragraph reference for every point. Mark any gap as a gap.
+Type a message↑

Then check what comes back

1Every tool greets you the same way: an empty box and a blinking cursor. This is where most people get stuck.

2Say who you are, and who the tool is to be.

3Say what you need. One task, in one sentence.

4Say what to work from. Give it the documents instead of asking it to remember the law.

5Say how you want the answer, and what to do with a gap.

6Then check what comes back. A bright, eager junior on their first day: quick, willing, and in need of someone keeping an eye on the work.

It isn’t a magic answer machine

Sometimes AI produces results that are so astonishingly good, it becomes tempting to think of it as an oracle. It isn’t. It predicts words that sound right, and most of the time that’s genuinely useful. But now and then it gets things confidently wrong, including inventing cases that don’t exist and quotes no judge ever wrote. In 2023, two New York lawyers made legal history that way in Mata v Avianca, Inc., 678 F Supp 3d 443 (SDNY, 22 June 2023); Damien Charlotin’s AI Hallucination Cases database has since passed 2,000 entries. The checking routine on this site exists for that reason.

Why this site exists · Check an AI-assisted draft · Useful links · About the author