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When Expert AI Prompts Become Evidence
Your expert uses generative AI to cut millions of documents down to something a human can actually read. Then the other side asks the question everyone has been dancing around: are the AI prompts and outputs discoverable? I walk through Conservation Law Foundation v Shell Oil Company (D. Conn.), where a magistrate judge answers “yes” and frames prompts as expert methodology under Rule 26, not some brand-new category of evidence. The twist: the ruling comes as a text-only minute order on the docket, and it’s currently stayed while the district judge reviews a Rule 72 objection, so the doctrine is moving in real time.
We get specific about what was used and why that matters. The expert team worked with GPT-4 models through Microsoft Azure OpenAI Service via the secure Azure API, an enterprise deployment with a very different risk profile than the consumer ChatGPT product. That technical choice intersects with discovery obligations, vendor retention questions, and preservation planning. If you are relying on “we don’t have it” defenses, we also talk about the court’s trust-but-verify approach and how sworn Rule 33 and Rule 34 responses can create Rule 37 exposure if anything later turns up in logs, metadata, or cloud systems.
From there, I pull out the practical drafting and strategy lessons: why Rule 29 stipulations must name AI prompts, AI queries, and AI outputs explicitly; why relabeling prompts as “search terms” won’t hold when the technology is generative; and how a prompt produced cold can be a gift to cross-examination unless the expert report explains the full AI methodology. We also connect the through line to Florida’s amended Rule 2.515, which puts citation accuracy and verification squarely on the signer, with sanctions on the table.
If you work with testifying experts, eDiscovery, or AI-assisted document review, this is the roadmap you want before the next motion to compel lands. Subscribe, share the episode with your team, and leave a review so more litigators can keep up with how AI discovery doctrine is being built.
Thank you for tuning in to Meet and Confer with Kelly Twigger. If you found today’s discussion helpful, don’t forget to subscribe, rate, and leave a review wherever you get your podcasts. For more insights and resources on creating cost-effective discovery strategies leveraging ESI, visit Minerva26 and explore our practical tools, case law library, and on-demand education from the Academy.
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By Kelly Twigger
5
88 ratings
Your expert uses generative AI to cut millions of documents down to something a human can actually read. Then the other side asks the question everyone has been dancing around: are the AI prompts and outputs discoverable? I walk through Conservation Law Foundation v Shell Oil Company (D. Conn.), where a magistrate judge answers “yes” and frames prompts as expert methodology under Rule 26, not some brand-new category of evidence. The twist: the ruling comes as a text-only minute order on the docket, and it’s currently stayed while the district judge reviews a Rule 72 objection, so the doctrine is moving in real time.
We get specific about what was used and why that matters. The expert team worked with GPT-4 models through Microsoft Azure OpenAI Service via the secure Azure API, an enterprise deployment with a very different risk profile than the consumer ChatGPT product. That technical choice intersects with discovery obligations, vendor retention questions, and preservation planning. If you are relying on “we don’t have it” defenses, we also talk about the court’s trust-but-verify approach and how sworn Rule 33 and Rule 34 responses can create Rule 37 exposure if anything later turns up in logs, metadata, or cloud systems.
From there, I pull out the practical drafting and strategy lessons: why Rule 29 stipulations must name AI prompts, AI queries, and AI outputs explicitly; why relabeling prompts as “search terms” won’t hold when the technology is generative; and how a prompt produced cold can be a gift to cross-examination unless the expert report explains the full AI methodology. We also connect the through line to Florida’s amended Rule 2.515, which puts citation accuracy and verification squarely on the signer, with sanctions on the table.
If you work with testifying experts, eDiscovery, or AI-assisted document review, this is the roadmap you want before the next motion to compel lands. Subscribe, share the episode with your team, and leave a review so more litigators can keep up with how AI discovery doctrine is being built.
Thank you for tuning in to Meet and Confer with Kelly Twigger. If you found today’s discussion helpful, don’t forget to subscribe, rate, and leave a review wherever you get your podcasts. For more insights and resources on creating cost-effective discovery strategies leveraging ESI, visit Minerva26 and explore our practical tools, case law library, and on-demand education from the Academy.