Imagine trying to find specific information in a sea of technical jargon, like searching for a needle in a haystack. This is the challenge of semantic search in low-resource, domain-specific languages. These languages, often rich with specialized terminology and industry-specific syntax, lack the extensive datasets needed to train effective search models. Think of the complex language used in fields like chemical engineering or legal proceedings – packed with acronyms, codes, and nuanced phrasing that general search engines struggle to understand. This research tackles this very problem, focusing on the automated creation of evaluation datasets for semantic search in such specialized languages. The traditional method of building these datasets is slow, expensive, and requires domain experts. The researchers, therefore, explored an innovative approach using 'ensemble learning.' Ensemble learning combines the strengths of multiple individual models, or 'weak learners,' to create a more powerful predictive model. Imagine a team of specialists, each with their own area of expertise, working together to solve a complex problem. In this case, the researchers combined multiple text encoders, each trained on different aspects of language, to create a more robust understanding of the specialized terminology. But the innovation doesn't stop there. They also incorporated a Large Language Model (LLM) to refine the relevance scores generated by the ensemble. The LLM acts like a final reviewer, checking the accuracy and relevance of the search results based on its broader understanding of language. This combined approach proved significantly more effective than using individual models alone, demonstrating a four-fold improvement in inter-coder agreement and a substantial boost in overall accuracy. This research has significant real-world implications. By automating the creation of these evaluation datasets, we can unlock the potential of semantic search in a wide range of specialized fields. This means more efficient research, faster information retrieval, and better decision-making in industries that rely heavily on technical language. However, challenges remain. Accessing commercial LLMs and navigating ethical and legal constraints around data privacy are crucial considerations. Future research will focus on refining the ensemble learning process, exploring multi-agent LLMs, and tailoring the approach to various languages and specific downstream tasks. This work opens up exciting possibilities for improving how we search and interpret information in the increasingly complex world of specialized languages.