This document delves into the advanced stages of developing a question-answering (QA) system for codebases, building on previous discussions about indexing and semantic code search. It highlights the use of LLM-generated comments to bridge code with natural language queries, enhancing both keyword and semantic searches. The text emphasizes the importance of selecting appropriate embeddings and vector databases, such as OpenAI's text-embedding-3-large or Jina-embeddings-v3, and outlines methods to refine retrieval accuracy, including hybrid search combining semantic and keyword-based techniques like BM25, and re-ranking using cross-encoders. Additionally, it introduces the HyDE approach to better align natural language queries with code through hypothetical document embeddings and discusses the implementation of these strategies using tools like LanceDB. The document concludes by reflecting on latency and accuracy improvements, and encourages experimentation with the complete implementation available on GitHub, aiming to provide valuable insights into creating effective code QA systems.