When The AI Product Becomes The Evidence

The evidence you need from an AI company might not be emails, chats, or a handful of prompt screenshots. It might be the machinery behind the product: the retrieval index, the operational database, and the logs that capture every interaction at scale. That shift changes what you ask for, how you scope it, and how you argue proportionality when the data volumes get huge.  We walk through Encyclopedia Britannica and Merriam-Webster v Perplexity AI, a Southern District of New York decision that reads like a plain English guide to retrieval augmented generation (RAG). We explain how an “answer machine” pulls content from a RAG database, hands it to a large language model, and generates outputs that can allegedly reproduce or paraphrase copyrighted works. We also break down Perplexity’s user activity log and why it can be the clearest record of what a user asked, what the system retrieved and ranked, what instructions went to the model, and what answer came back.  From there, we translate the holding into a practical discovery playbook: why copyright registration dates can define the right time window for technical discovery in a retrieval time case, how courts apply Rule 26 relevance and proportionality to limit massive productions, and how cost shifting can hinge on whether your cost numbers stay consistent and well supported. If you litigate, manage eDiscovery, or advise clients using AI tools, you’ll leave with sharper vocabulary and better requests. Subscribe, share the episode with a colleague, and leave a review if these case driven takeaways help you. 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 (https://minerva26.com/) and explore our practical tools, case law library, and on-demand education from the Academy.