AI Deposition Summary and Prep: Prompts, Tools and Guardrails

On 9 September 2026 the New Mexico Supreme Court fined Santa Fe lawyer Stephen Aarons $5,000, held him in contempt and referred him to the disciplinary board. His brief in a murder appeal “contained false testimony from wholly fabricated witnesses”, including “fictional statements that the shooter was wearing dark pants and a white shirt”. He had fed the transcript to ChatGPT expecting, in his words, “a bulletproof summary”.

That is the one bad way to do an AI deposition summary: hand a transcript to a consumer chatbot, ask for a summary, file what comes back. Everything else about the task is favourable. A transcript is a closed universe: the model does not need to know the law, find authority or guess at facts outside the four corners; it needs to read, extract and cite. In Vals’ 2025 legal AI benchmark, transcript analysis was where the tools beat the lawyers by one of the widest margins: Harvey scored 77.8%, vLex’s Vincent 64.8%, the lawyer baseline 53.7%.

The difference between Aarons and a good result is not the model. It is three layers, one prompt each, and a page-line citation in every row.

Why transcripts suit AI, and the one prompt that ruins it

The question “AI for summarising case depositions” comes up on r/legaltech in almost those words, and the honest answer is that the task suits AI for two reasons. The source is bounded: you can instruct the model to use only the transcript, quote rather than paraphrase, and cite a page and line for every claim. And the volume is real; a federal judge in the late-2025 survey of judicial AI use put it simply: “Summarizing trial transcripts… is a huge time saver.”

The prompt that ruins it asks for everything at once. Clio’s prompt-engineering guide uses this as its example of overloading: “summarise this deposition, identify credibility issues, compare it to the plaintiff’s affidavit, and draft follow-up questions” produces output that “does none of them particularly well”. Yet the same guide’s headline deposition prompt bundles four tasks too: “Summarize this 80-page deposition of the plaintiff’s HR manager. Identify statements that contradict the verified complaint, flag admissions relevant to our motion for summary judgment, and note any lines of questioning we should pursue in follow-up. Output as a table with page and line citations.” The last sentence is the valuable part. Keep the citation discipline; split the tasks.

Layer 1: the AI deposition summary with page-line citations

The first prompt extracts; it does not judge. For a 300-page deposition, split by session or topic, because accuracy drops as the context fills.

Use only the attached transcript of the deposition of [the HR manager, "DW3"]. Do not use any other knowledge of the case.
Produce: (1) a one-paragraph neutral overview of what the witness was asked about; (2) a table of every substantive statement on [the termination decision], with columns: Page:line | Verbatim quotation | Topic | Type (fact asserted / denial / "does not recall" / opinion); (3) every document, person and date the witness mentioned, with page:line; (4) topics the witness could not recall, with page:line.
Quote, do not paraphrase, in the quotation column. If you cannot find a page:line for a statement, omit the statement. Do not assess credibility and do not draw conclusions.

Two practical rules. Clean the text first: an r/legaltech user warns that without basic OCR clean-up “you get hallucinated dates, mangled drug names, and unusable citations”. And keep one transcript per conversation; Justia’s warning that long chats become “haunted by the ‘ghosts’ of past prompts” is doubly true when two witnesses’ answers can blur.

Layer 2: contradictions within and across witnesses

The second layer compares: within a deposition, internally inconsistent answers; across sources, the deposition against the verified complaint, an affidavit or an expert report. Everlaw’s AI Assistant does the within-deposition version as a built-in function (“witness discrepancies”), and Harvey reports that partners at Mijares find inconsistencies in expert reports “in minutes”.

<deposition>…</deposition>
<prior_statement>[the verified complaint / affidavit / expert report]</prior_statement>
Compare the two sources. Output a table: Item | Prior statement (paragraph and verbatim words) | Deposition (page:line and verbatim words) | Nature of inconsistency (direct contradiction / omission / change of detail / different date or number) | Topic.
Then a second table of statements within the deposition that are inconsistent with each other, same columns.
Include only pairs where both quotations are verbatim. Do not assess credibility, rank importance, or speculate about why the witness changed the account.

Read the output as candidates. A “different date” row may be a transcription error; a “change of detail” may be an honest clarification. Deciding which pairs are contradictions is the lawyer’s job, which is why the prompt forbids credibility assessments; the model that decides credibility for you is the model that tells you what you want to hear.

Layer 3: cross-examination outline and impeachment matrix

Only now do you ask for questions, and only from the verified rows of layers 1 and 2. Harvey’s published cross-examination prompt gets the structure right: “draft targeted cross-examination questions for the opposing expert. Focus on assumptions, methodology, inconsistencies with record evidence, unsupported conclusions, and admissions useful to our motion strategy. Group questions by topic and include the source passage that supports each line of questioning.” What turns it into an impeachment matrix is a column for the expected answer and the fallback if the witness denies.

Using only the verified inconsistency table <table>…</table> and the deposition transcript <deposition>…</deposition>, draft a cross-examination outline for [the witness]. Group by topic. For each point: the closed, leading form of the question (one fact per question); the supporting passage (document and page:line); the answer we expect; and the impeachment step if the witness denies (which page:line to read into the record). Include no question without a supporting passage and no topic that is not in the table.

Models write open questions by instinct. “Closed, leading form, one fact per question” helps; you will still rewrite them in your own voice. The outline is a scaffold, not a script.

Prompts that work, and where each came from

Every one shares the constraint that makes AI deposition prep safe: the answer must point to a page. The prompt library holds the full versions.

Tools: Gemini Notebook, Everlaw, CoCounsel, Claude Projects

Ernie Svenson’s line about Gemini Notebook is the right mental model for all of them: it “is built for synthesis, not free jazz.” His workflow ends with “the most important step: you spot-check.” The Gemini Notebook guide for lawyers walks through the set-up.

The Aarons warning: fabricated witnesses from a transcript upload

Three things went wrong, and the three-layer method prevents each. The prompt asked for a “bulletproof” summary, which invites confident prose rather than cited extraction; layer 1 demands a page:line for every statement. The output was not checked; the citation column makes checking a matter of seconds per row. And the summary went straight into a filing; the layers keep extraction, comparison and advocacy in separate documents, each verified before the next. Fabricated testimony is a cousin of fabricated case law; the checking habit is the one in how to verify AI legal citations.

Confidentiality: protective-order material and consumer tools

A deposition transcript is discovery material, and in 2026 two federal courts said what may not be done with it. In Jeffries v. Harcros Chemicals (D. Kan., 25 March 2026) the court amended the protective order to ban public, “open” AI tools for all discovery materials, confidential or not, because it is “practically impossible” to claw back data once processed. In Morgan v. V2X (D. Colo., 30 March 2026) the order bars putting confidential information into any AI platform unless the provider is contractually prohibited from “(1) storing or using inputs to train or improve its model; and (2) disclosing inputs to third parties except where essential”, which the court accepted “practically bars the use of most ‘low-to-no-cost’ AI tools”. Akin’s summary of the rulings is the best short read.

One more line, from Australia: the NSW Supreme Court’s Practice Note SC Gen 23 bars generative AI from generating the content of affidavits and witness statements. Summarising what a witness said is a good AI task; drafting what a witness will say is not.

Checklist before the deposition

1. Protective order read; tool tier chosen to comply with it; nothing in a consumer plan.
2. Transcript cleaned (OCR corrected, witness labelled); one witness per conversation or notebook.
3. Layers 1 to 3 run in sequence; every page:line you will use opened in the transcript; each contradiction pair classified by you, not the model; questions rewritten in your voice.
4. Weakest points mooted: the oral-argument and moot guide shows how to have a model play the witness or the judge.
5. Nothing AI-generated enters a filing until a lawyer has read the cited page for every statement.

Where the deposition sits inside a 50,000-document production, the work belongs in the generative AI document review workflow; the wider picture is in the litigation lawyers’ guide.

Litigators in AI Lab for Lawyers build this workflow live: a Gemini Notebook deposition notebook, the three prompts on anonymised material, and the spot-check that shows whether the citations hold.

Where to go next: the case chronology guide covers the step before the deposition, turning the email dump into a dated timeline, and how lawyers actually use AI sets deposition prep beside the other litigation workflows in the use-cases hub.