AI can turn a 200-page deposition transcript into something usable in minutes—but the same speed that makes it attractive is what can get you in trouble. If you’ve read enough lawyer-to-lawyer threads on Reddit and litigation support forums, you’ve seen the same warnings repeat: AI will “confidently” invent a quote, misattribute a speaker, flatten nuance (especially around objections), and quietly omit the one exchange that matters. The safe way to use AI for deposition summaries is to treat it like a junior who works fast but needs supervision: you build a verification workflow that forces every key point back to the transcript, and you keep an audit trail that proves what you did when someone challenges it.
Build a Verification Workflow for AI Summaries
Start by defining what the AI is allowed to do and what it is not. In practical forum advice, the winning approach is: let AI propose structure and candidate issues, but never let it be the final authority on “what was said.” Your prompt should require timestamp/line or page:line citations for every factual assertion and every quote, and you should explicitly instruct the model to say “Not found in transcript” rather than guess. If your transcript has line numbers, demand them; if it doesn’t, convert it to a format that does (many litigation tools export with page/line, and some transcript-to-text workflows preserve them).
Next, build a two-pass verification routine that mirrors how careful teams actually operate. Pass one is “mechanical validation”: check that each quoted passage exists, the speaker is correct, and the surrounding context doesn’t change the meaning (a common failure is summarizing an answer while ignoring that it was immediately corrected). Pass two is “legal relevance validation”: confirm that the summary’s issue framing matches your case theory and jurisdictional standards—AI frequently overgeneralizes (“admitted liability”) when the testimony is narrower (“could have,” “might have,” “I don’t recall”). A practical trick borrowed from power users: have the AI produce (1) a bullet summary, (2) a “key admissions” table, and (3) a list of “uncertain or low-confidence items” so your reviewer knows where to spend time.
Finally, standardize what “done” looks like with a checklist that’s hard to shortcut. The checklist should include: (a) every “key fact” has a citation; (b) every “admission” has a verbatim quote + citation; (c) objections are represented accurately (e.g., “form” vs. “privilege” and whether an answer was given); (d) testimony changes are captured (corrections, clarifications, “let me rephrase”); and (e) a short “what this doesn’t establish” section to prevent the summary from overselling. This last item comes up often in community discussions because the real risk is not just fabrication—it’s the subtle drift from testimony into argument.
Create an Audit Trail That Survives Scrutiny
Treat your AI deposition summary like a work product that may need to be defended internally (or, in rare cases, externally). That means you preserve inputs, outputs, and the verification steps. Save the exact transcript version used (including exhibit references if available), the exact prompt(s), the model/tool identifier, and the raw AI output before human edits. People who’ve been burned online often mention the “mystery edit” problem: once a summary is in Word with tracked changes off, it’s hard to prove what came from the transcript, what came from AI, and what came from an associate’s rewrite. Avoid that by keeping a read-only “AI draft” file and a separate “verified final” file.
Make the citations do double duty as your audit trail. Instead of sprinkling citations only at the end, use a structure that ties each bullet to a page:line range and makes spot-checking easy. A simple format that holds up: Issue → What the witness said (quote or tight paraphrase) → Page:Line → Notes (objection/context) → Reviewer initials/date. If you work in a team, add a “verification status” column (e.g., Unverified / Verified / Needs follow-up) and require that “Verified” means a human actually opened the transcript and checked it, not just “it looks right.” This is exactly the kind of procedural rigor people advocate in practitioner forums because it prevents the quiet acceptance of AI errors.
Finally, log your process like you would for eDiscovery defensibility—because the logic is similar. Keep a lightweight “Summary QA Log” that records: what transcript was summarized, which prompts were used, who reviewed, what percentage was spot-checked (and why that sampling was reasonable), and what corrections were made. If you’re using a tool that supports it, export activity logs and version history. If you’re not, you can still create defensibility with disciplined file naming, tracked changes, and a short memo-to-file describing your verification method. The goal isn’t to create bureaucracy—it’s to ensure that if a partner, client, or opposing counsel ever challenges an “admission” you cited, you can immediately point to the transcript line and show the human verification path.
Using AI for deposition summaries can be a competitive advantage, but only if you design for the failure modes people keep reporting: hallucinated quotes, misattribution, and overconfident legal conclusions. A verification workflow forces every meaningful statement back to the transcript, and an audit trail preserves how the summary was generated and checked. Do those two things consistently and AI becomes what it should be in litigation support—a fast drafting assistant that never replaces human accountability.