Journal of Data Processing Dit Suthiwong 16 1 2026 https://doi.org/10.6025/jdp/2026/16/1/16-30 https://www.dline.info/jdp/fulltext/v16n1/jdpv16n1_2.pdf This paper presents an empirical study on the impact of LLM-assisted metadata enrichment on the reliability and performance of enterprise grade Generative AI (GenAI) systems, using the OECD IFCMA Climate Policy Dashboard as a real world testbed. The dataset comprising over 1,600 heterogeneous climate policy instruments across 43 approaches and multiple countries exhibits significant semantic inconsistency and incomplete metadata, reflecting common challenges in multinational data environments. The authors implement a three stage GenAI enabled pipeline: (1) definition of a structured metadata schema, (2) LLM-driven semantic enrichment to infer missing fields and harmonize terminology, and (3) a metadata aware Retrieval Augmented Generation (RAG) system that leverages enriched context for grounded responses. Quantitative evaluation demonstrates a statistically significant improvement in metadata completeness from a mean of 0.41 to 0.83 (p < 0.001) and a marked increase in cross country semantic consistency, with cosine similarity rising from 0.64 to 0.85 for carbon pricing policies. These enhancements directly translate into tangible RAG performance gains: retrieval precision improves by 25.8%, answer faithfulness by 23.5%, and hallucination rates decline by 41.9%. Crucially, correlation analysis confirms a strong positive relationship between metadata quality and GenAI reliability ( > 0.7). The study positions high quality metadata not as auxiliary documentation but as a foundational architectural component that enables observability, explainability, and trust in high stakes GenAI applications. By bridging a critical gap between conceptual frameworks and empirical validation, this work establishes metadata centric design as essential for scalable, governance aligned, and reliable enterprise AI systems.