A pharmaceutical company is building a RAG application over a corpus of internal drug-research documents that are dense with specialized biomedical terminology and abbreviations. During evaluation, the team notices that retrieval frequently returns loosely related passages and misses the technically correct sections, even though the vector index and chunking are configured correctly. As the Generative AI Engineer, which change to the RAG design is MOST likely to improve retrieval relevance?