Efficiency Gains in Rare Book Assessment: Evaluating Generative AI as an Adjunct Approach

100 rare books chosen at random Each book was photographed six times in the following areas: Front cover and spine Front endpapers (both) First random set of pages Second random set of pages Back endpapers (both) Back cover and spine The following data was collected about each book: Barcode Condition of the text block Gutter margin width Paper type and condition Binding type and condition Suggested preservation/conservation actions Damage prior to assessment (Y/N) Foxing Present (Y/N) Prior Water Damage (Y/N) A human experienced with assessing the condition of rare books evaluated each book. Six photographs were taken of each book and were fed into Gemini and ChatGPT to assess for the same assessments that the human performed. The prompt that was used: “Using the uploaded images as a guide generate a preservation assessment report using the following parameters: Condition of the text block Gutter margin width Paper type and condition Binding type and condition Suggested preservation and conservation actions for this book”