The text discusses strategies for improving the retrieval accuracy of Retrieval-Augmented Generation (RAG) pipelines, particularly when users provide minimal input, such as a couple of lines or words, for tasks like generating a sequel to a song. The document highlights the limitations of using vanilla RAG, which often returns multiple results from different sources, resulting in a loss of context. To address this, the text suggests two approaches: using a Parent Document Retriever to find and pass the most relevant chunk's parent document to the language model, and creating larger chunks to retrieve instead of whole parent documents, thus balancing between context preservation and size constraints. The text details the implementation using tools like LanceDB, LangChain, and embedding functions, and provides an example with Eminem song lyrics, demonstrating how to manage document chunks and retrieval processes effectively.