AI Oral Argument Prep: Moot Your Case Without Being Fooled by a Confident Wrong Answer

When Judge Brantley Starr wrote the first American standing order on generative AI in May 2023, he was not hostile to the technology. His order says these platforms “have many uses in the law: form divorces, discovery requests, suggested errors in documents, anticipated questions at oral argument. But legal briefing is not one of them.” The judge who made lawyers certify their filings put AI oral argument prep on the approved side of the line.

He was right. A moot is the one task where a model’s worst habit, confident fluency, is what you want from the bench, and where its worst failure, an invented case, cannot reach a filing because you never meant to cite anything it says.

Client consent first, then a confidential workspace

The Virginia appellate lawyer whose October 2025 write-up is the best public description of this workflow starts where most guides skip: tell the client and get consent, because the briefs, key cases and record excerpts are about to be uploaded.

ABA Formal Opinion 512 requires informed consent before client information goes into a self-learning tool, and says engagement-letter boilerplate is not enough. In United States v. Heppner (S.D.N.Y., February 2026) Judge Rakoff held that a defendant’s own exchanges with consumer Claude were protected by neither privilege nor work product, adding that counsel-directed use “might arguably” be different. So: a no-training tier (ChatGPT Business, Claude Team or a legal platform), counsel directing the exercise, and a clear view of what waives privilege before you start.

A ChatGPT or Claude Project with the briefs, the controlling cases and the record excerpts is enough; the guide to Claude Projects and custom GPTs covers the mechanics. Suffolk Law built its students a custom GPT “already prompted to act as a judge”: upload the pleadings and exhibits, state your side, about ten exchanges a day on a free account.

Ask for the panel’s concerns, a table of positions and 25 questions

The Virginia workflow opens with one sentence and asks for deliverables rather than conversation: likely concerns, critical record cites, a table of positions, the counter-arguments neither brief addressed, each side’s strongest arguments ranked, 25 prep questions and, last, a hypothetical opinion.

I am counsel for the appellant, and I am preparing for oral argument before [court] on [date]. In this project you have both briefs, the key authorities and the record excerpts. Cite nothing that is not in the project.
Produce, in order: (1) the panel's likely concerns, from the briefs alone; (2) what the judges will want to learn from me that the briefs do not settle; (3) a table comparing appellant's and appellee's positions issue by issue, with the record cite each relies on; (4) counter-arguments neither brief addresses; (5) each side's three strongest arguments, ranked; (6) 25 questions I should prepare for, hardest first.

Read the hypothetical opinion as a diagnostic: if the model decides the case on a ground you have not briefed, that is your first prep question.

When the questions are too polite, say so: models drift towards agreement, and OpenAI rolled back a GPT-4o update in April 2025 for being “overly flattering or agreeable”. A moot judge who agrees with you is worse than none.

The moot: one question at a time, in the voice of a named judge

Now switch AI oral argument prep from deliverables to dialogue. Justia’s role-play prompt is the cleanest published version; its two design choices, one question at a time and a stop phrase, are what keep the model from summarising or dropping character.

Act exclusively as a [sceptical appellate judge / strict arbitrator / Vorsitzende Richterin] hearing [brief case overview], where I appear for [party]. You have read my brief and the opposing brief in this project.
Ask me one challenging question at a time about my argument, then wait. If my answer is evasive or incomplete, follow up. Do not break character, do not summarise, and do not step out of the conversation until I say "Time Out". Begin with the question you think I least want to be asked.

The Virginia lawyer’s refinement is to run the moot “in the voice of” a named judge, Elena Kagan, John Roberts or, for a first-rate opponent, Paul Clement. Voice is style, not prediction. If the panel is known, add a prediction pass with public materials only: each judge’s opinions on related issues, argument transcripts and speeches, and a request for the three questions each is most likely to ask, the hardest question each could ask, and a record-based answer to each, with the instruction to say “no material” rather than generalise. That is what Katherine Forrest at Paul Weiss did with Harvey for named Ninth Circuit judges, and her result is the strongest endorsement on record: “remarkably accurate questions” that “would have taken a mid-level associate weeks to compile”, as she told Bloomberg Law.

Keep the moot in its own chat; its only job is to ask. Attacking your written argument belongs in the adversarial self-critique workflow, since a model asked to draft, critique and rewrite at once degrades at all three.

Voice mode: arguing out loud on the commute

Reading a hard question is not hearing it on your feet. ChatGPT’s voice mode turns the Justia prompt into something close to a live bench, and it is the only way to practise the pause, the concession and the pivot.

Adam Unikowsky, the Supreme Court advocate who had earlier scored Claude 3 Opus’s disposition of Smith v. Spizziri from the briefs alone as “10/10”, went furthest: he fed Williams v. Reed into Claude 4 Opus and ChatGPT Advanced Voice with an ElevenLabs voice to recreate his own argument. His write-up matters for the limit as much as the fluency: pressed on a flawed question, the model still “fabricated a response” rather than conceding. A human judge who does not know says so. The model performs an answer.

Record the session, because the value is in the review, and treat any statement of law the “judge” makes as unverified; a fluent voice makes a wrong holding sound more right, not less.

The three-agent moot and the audit trail

The most elaborate published set-up is a litigator’s on r/legaltech, September 2026: “Set up in Claude to spin up two agents, one for each side then make arguments to a third judge agent who makes a ruling between the two. That ruling is then sent to CoCounsel or perhaps Midpage for deep research analysis to confirm, get a strength rating or find blind spots etc.” He requires “all arguments between the three to be written to an audit trail”, and every cited case to “come out clean with a 2-3 service waterfall confirming and sheperdizing”. Verdict: “I love reading the ‘hearing’ transcripts. It’s actually really helped me prepare for oral argument.”

Three chats in one project copy the structure without agents: appellant, respondent, bench.

You are the presiding judge. Here are appellant's argument (<appellant>) and respondent's argument (<respondent>) on [issue]. Ask each side one question at a time; I will paste the answers back. After two rounds, write a bench memo: which side had the better of each issue, which question neither side handled, and which record cite you would want before deciding. Cite nothing outside the two arguments.

The transcript is study material, not work product: every authority any chat mentions is [VERIFY], and nothing crosses from the moot into a filing without passing through a primary database.

What Professor Woods found: adept at answers, “not terribly inquisitive”

Prof. Jayne Woods tested ChatGPT the way a coach would: give it the facts, ask for “an oral argument outline”, then “What do you think an appellate court would ask?”, which produced around 30 questions, then “can I have more?”. Her assessment, in an interview with Kowal Law Group, is the most honest on record: “very adept at answering questions” but “not terribly inquisitive”.

That asymmetry is the lesson of AI oral argument prep. A model is trained to answer; a judge’s job is to ask. Left alone it produces reasonable questions and runs out of curiosity, so you keep prodding, feed it the opposing brief and supply the questions it never thinks of.

A pro se litigant reached the same place. Staci Dennett, facing a debt-collection dispute, told NBC News she asked ChatGPT to “pretend it was a Harvard Law professor and… rip my arguments apart”; she settled. Critic, not oracle, is the instinct that keeps lawyers out of the hallucination database.

Paul Weiss and Harvey: “remarkably accurate questions”, and no hard metrics

Paul Weiss began testing Harvey in January 2023 and, some eighteen months in, told Bloomberg Law it “isn’t using hard metrics like time saved”, because verification “makes any efficiency gains difficult to measure”. Forrest’s use case stood out because a plausible hard question is useful even when its reasoning is not.

This is one of the litigation workflows we run live in AI Lab for Lawyers, on anonymised briefs, with the confidentiality settings on screen first.

Where to go next: the deposition preparation guide applies the same one-question-at-a-time discipline to witnesses; the ChatGPT for lawyers guide covers the tier settings that decide whether your briefs train a model; and the prompt library holds these moot prompts beside the other litigation workflows in the use-cases cluster and the overview of how lawyers use AI. The live sessions of AI Lab for Lawyers are where this becomes a habit.