We Need to Chat: A presentation about real-world AI use cases

Recent advances in artificial intelligence and large language models (LLMs) have created new opportunities for enhancing digital repositories and discovery. This panel presents three implementations that have moved beyond theoretical exploration to AI solutions that solve real-world problems. Northwestern University Libraries developed a discovery interface using Retrieval Augmented Generation (RAG) that enables natural language querying of IIIF-based digital collections, leading to an IMLS-funded open-source solution. Emory University Libraries implemented a hybrid intelligence approach for metadata enhancement, leveraging AWS infrastructure and LLMs to improve description while maintaining human oversight and addressing historical biases. Harvard Library's Collections Explorer project employs semantic search and generative AI to revolutionize special collections discovery, building on insights from their "Talk with HOLLIS" pilot and collaboration with Mozilla.ai. Through these case studies, the panel will examine practical implementation strategies, architectural decisions, and lessons learned while deploying AI in repository environments. Discussion will address how these technologies can meet evolving user expectations while maintaining institutional values and responsibilities. The session will provide attendees with concrete examples of successfully operationalizing AI tools for discovery and metadata generation in library contexts.