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You have a deposition in four hours, and the expert witness report is 300 pages with exhibits, methodology notes, and quiet qualifiers in footnotes. The job is not to produce a fluent paragraph. The job is to produce a usable work product you can question from, issue spot against, and hand to a partner without re reading the entire report. In this course, we use AI to generate a first pass deliverable like an AI deposition prep summary, an AI issue list, or an AI section by section outline, then we treat that output as a draft that has to earn reliance through attorney review.
A deliverable first approach starts by naming what the summary is for, because purpose controls coverage. A deposition prep summary needs assumptions, limitations, and what would change the conclusion if the facts moved. A client update summary needs the bottom line and uncertainty ranges stated in plain language. A regulatory guidance summary needs defined terms and conditions, not marketing headings. The AI becomes useful when it is aimed at an explicit deliverable, not at the generic task of making something shorter.
For deposition prep, the AI summary you want is structured around the decisions you must make. What are the expert’s core opinions, what inputs the opinions depend on, and what the expert is careful not to claim. You are looking for the parts that let you form clean cross questions, including assumptions, boundary conditions, and any limitation that narrows the opinion’s reach. That is where AI summarization often fails silently because the missing detail reads like a style choice rather than a gap. Here is an example AI summary excerpt to examine, with the report sections it claims to cover. Let’s inspect what the summary leaves out.
Verify coverage before reliance. An AI summary is a draft deliverable. Before it is communicated to a client, used to prepare a witness examination, or incorporated into a filing, the attorney must check it against the source for coverage of the items the deliverable requires, including footnotes, qualifications, and caveats.
In a manual workflow, you build a working outline, skim for issue dense sections, and create your own deposition prep or client update deliverable as you go. In an AI assisted workflow, you still decide what the deliverable must contain, but you delegate the first pass extraction and organization to an AI outline or AI section summary, then you review and tighten it where the stakes are. The point of AI here is not to replace judgment. It is to compress the time from document receipt to a structured starting product you can verify and improve. Next we will compare the two baselines across the constraints that matter in practice.