The text discusses improving chunk retrieval performance by integrating contextual retrieval techniques, particularly in tasks where users search in one language and the context is in another, such as text-to-code generation. It highlights the limitations of BM-25 matching and introduces a solution involving Retrieval-Augmented Generation (RAG) with hybrid search, combining BM-25 and vector indexing. This approach involves creating context for each chunk using a language model, which is then appended to the original chunk, improving search relevance. The document also emphasizes the cost benefits of prompt caching, where a document is loaded into a cache once rather than repeatedly for each chunk, significantly reducing token costs. The process is demonstrated using a database schema that includes both the raw chunk and its generated context. The text concludes by noting improved retrieval performance, with top results coming from the same document, and suggests using the LanceDB Reranking API for even more refined search results.