A Federally Funded Research and Development Center struggled to retrieve relevant information from a vast archive of scientific papers, leading to long search times and risk of knowledge loss when key documents could not be found. Researchers faced inefficiencies that hindered productivity and limited access to valuable historical insights. To address these challenges, Enterprise Knowledge implemented a solution that leveraged large language models to enhance content cleanup and improve metadata quality. By adding descriptive “about-ness” tags and refining document classification, the organization improved discoverability, reduced search effort, and enabled more efficient access to critical research materials across its repository.

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