AI để tóm tắt và chuẩn bị lắng đọng

Deposition work involves three recurring jobs that AI can speed up. Summarizing. Traditional deposition summaries come in three forms: page-line summaries that move through the transcript in order, topical summaries that group testimony by issue, and narrative summaries that tell the witness's account in prose. AI can produce any of them in minutes. Quality depends on the input. A clean certified transcript with page and line numbers preserved produces citable summaries, while a rough draft transcript may contain errors the model will faithfully repeat. Finding inconsistencies. Given several transcripts, prior statements, interrogatory answers or documents, AI can flag where a witness contradicts himself, another witness or the paper record. This is valuable because contradictions are often spread across hundreds of pages. It is also where false positives are common: a model may treat a clarified answer as a contradiction, miss context from an objection or an instruction not to answer, or confuse speakers. Every flagged inconsistency needs a human reading of both passages. Preparing outlines. From the pleadings, key documents and earlier testimony, AI can draft topic lists and question sequences for taking a deposition, or practice questions for preparing your own witness. Lawyers still decide strategy: what to lock in, what to save for trial and what not to ask. Two professional constraints apply. Transcripts often contain material designated confidential under a protective order, which may limit who can receive it; uploading to a vendor must fit those terms and the duty of confidentiality. And anything used to impeach a witness or cited in a motion must be quoted from the certified transcript with the exact page and line. A common misconception is that a summary can replace reading the transcript of a key witness. For central testimony, a summary shows where to look; it does not replace reading.