Automated Metadata Generation Using Large Language Models: A GPT-4 Case Study for Enterprise Data Profiling

Modern data ecosystems face unprecedented challenges in metadata management as data volumes expand and schemas evolve rapidly across heterogeneous sources. Traditional profiling techniques relying on schema inspection, statistical analysis, and manual annotations fail to capture semantic context and domain-specific meaning inherent in enterprise datasets. Large language models, particularly GPT-4, present a transformative opportunity for intelligent data profiling by generating context-aware, human-readable column descriptions from raw tabular data. This article demonstrates GPT-4's capability to produce semantically coherent metadata across diverse schema types, from transactional databases to semi-structured logs, achieving superior results compared to conventional profiling tools. The implementation leverages few-shot prompting and context conditioning to enhance description quality while addressing practical concerns, including hallucination control, sensitive data handling, and workflow scalability. Integration strategies for embedding LLM-based profilers into existing data catalogs, such as Unity Catalog and Apache Atlas, enable automated metadata enrichment at scale. Results indicate significant improvements in data discoverability, governance readiness, and adoption of self-service analytics when organizations deploy AI-augmented profiling pipelines. This work establishes a foundation for next-generation data infrastructure where generative AI bridges the gap between technical schemas and business understanding, ultimately reducing time-to-insight and empowering data-driven decision making across the enterprise.