This research introduces an automated solution for generating high-quality metadata descriptions to improve data catalog searchability and accessibility in enterprise environments.

Key Findings:

• Addresses the critical challenge of inadequate metadata that limits data discovery in organizational catalogs
• Leverages Retrieval Augmented Generation (RAG) with LLMs to create contextually relevant asset descriptions
• Enables scalable metadata curation to enhance data governance and accessibility
• Improves security and governance by making data assets properly documented and discoverable

For organizations struggling with data findability, this approach offers a practical path to transform underutilized data catalogs into valuable business assets while maintaining proper security controls.

Leveraging Retrieval Augmented Generative LLMs For Automated Metadata Description Generation to Enhance Data Catalogs