What is the primary weakness of the initial hybrid retrieval approach described in the transcript?	It has weak points in the retrieval process, failing to accurately prioritize the most relevant sections for highly specific queries.
What technique is introduced to improve the accuracy of the retrieval process?	Re-ranking.
Where does the re-ranker fit into the overall retrieval pipeline?	It is a post-processing step that occurs after the initial search results from the vector index and BM25 index have been merged.
What is the core task of the re-ranker when processing search results?	To take the initial set of related documents and reorder them based on their relevance to the user's specific question.
What type of model is used to perform the re-ranking task?	A Large Language Model (LLM), such as Claude.
Why are Document IDs used instead of the full text chunks in the re-ranking prompt?	To improve efficiency and reduce latency by asking the LLM to return only the IDs, rather than the full text of every chunk.
What is the primary benefit of implementing a re-ranking strategy?	It significantly increases the accuracy of the search pipeline.
What is the primary drawback or trade-off of implementing a re-ranking strategy?	It increases the latency of the search pipeline because it requires an additional call to the LLM.