A Generative AI Engineer is building a RAG application over a corpus of long internal policy documents. Each document covers multiple unrelated topics (e.g., a single HR handbook covers benefits, leave policy, and code of conduct). During testing, retrieval frequently returns chunks that mix relevant and irrelevant content, and the LLM produces answers that blend unrelated topics. Which change to the retrieval architecture will most directly improve the relevance of retrieved context?