Classical search engines using indexing methods in data infrastructures primarily allow keyword-based queries to retrieve content. While these indexing-based methods are highly scalable and efficient, due to a lack of an appropriate evaluation dataset and a limited understanding of semantics, they often fail to capture the user's intent and generate incomplete responses during evaluation. This problem also extends to domain-specific search systems that utilize a Knowledge Base (KB) to access data from various research infrastructures. Research infrastructures (RIs) from the environmental and earth science domain, which encompass the study of ecosystems, biodiversity, oceanography, and climate change, generate, share, and reuse large volumes of data. While there are attempts to provide a centralized search service using Elasticsearch as a knowledge base, they also face similar challenges in understanding queries with multiple intents. To address these challenges, we proposed an automated method to curate a domain-specific evaluation dataset to analyze the capability of a search system. Furthermore, we incorporate the Retrieval of Augmented Generation (RAG), powered by Large Language Models (LLMs), for high-quality retrieval of environmental domain data using natural language queries. Our quantitative and qualitative analysis of the evaluation dataset shows that LLM-based systems for information retrieval return results with higher precision when understanding queries with multiple intents, compared to Elasticsearch-based systems.