SubjectSeeker AI: No-Code AI for Subject Matching Rare Books at KU Leuven Libraries Special Collections

KU Leuven Libraries’ Special Collections manages a unique collection of approximately 50.000 early printed books. While accurate subject assignment is essential for the discoverability and scholarly use of the collection, categorising this large dataset of works is resource-intensive when done manually. This paper details the development and impact of SubjectSeeker AI, a no-code AI Agent for subject matching rare books. It compares SubjectSeekers’s performance to that of other AI solutions and shares practical insights for other institutions looking for accessible ways to use AI to enhance their library collections. SubjectSeeker AI was not the first experiment with AI at KU Leuven Libraries Special Collections. Already in 2020, we started exploring AI-based methods to support the process of subject matching with the help of a company that specializes in strategies for digital heritage and data services. An initial AI model, trained on preset rules and title information, aimed to predict relevant subjects. However, the model’s effectiveness was limited: the often unique, multilingual, and archaically spelt titles posed challenges for accurate matching, resulting in substantial time spent on corrections. The emergence of ChatGPT and other Large Language Models (LLMs) with direct access to internet data marked a turning point. Quick experiments revealed that simply providing title and author information to ChatGPT produced better results than our previous professional AI model. Importantly, adopting a no-code AI solution empowered staff members to perform subject matching themselves, eliminating the need to rely on external companies for both development and execution. Building on these insights, we collaborated with KU Leuven Libraries’ AI Librarian to develop and test SubjectSeeker AI. Our process involved developing, testing, and implementing the AI agent, with iterative adjustments based on initial results. As KU Leuven has a contractual agreement with Microsoft Copilot, ensuring that inputs and outputs remain private and cannot be used to improve the model further, the agent was developed with a M365 Copilot license. Throughout the process, we leveraged supporting documents such as the complete subject list, previously matched titles from the database, and in-house matching guides based on staff expertise – including lists of authors and keywords linked to specific subjects, as well as book codes mapped to the subject list. Our subject list is based on the STCV project (The Bibliography of the Hand Press Book in Flanders, stcv.be/en), an online database managed by the Flemish Heritage Libraries, which is enriched with a controlled vocabulary for subject keywords. SubjectSeeker AI demonstrated notable improvements in matching appropriate subjects for rare books, outperforming our earlier AI model in both accuracy and usability. This paper offers a comprehensive overview of our process, covering the development, testing, and implementation of the AI Agents, along with the adjustments made to our initial plans. We will present the SubjectSeeker AI’s performance in matching relevant subjects, compare it with our previous AI solution, and showcase our agent’s instructional prompt. Since our goal is not only to describe our specific use case but also to demonstrate how other institutions can apply AI Agents for topic matching, we will conclude with lessons learned and recommendations for library institutions.