A Generative AI Engineer is developing a RAG system for their company to perform internal document Q&A for structured HR policies, but the answers returned are frequently incomplete and unstructured. It seems that the retriever is not returning all relevant context. The Generative AI Engineer has experimented with different embedding and response generating LLMs but that did not improve results.
Which TWO options could be used to improve the response quality? (Choose two.)
A. Add the section header as a prefix to chunks By tagging each chunk with its section heading (e.g. “Vacation Policy,” “Leave of Absence,” etc.), you give the retriever additional semantic signals so it can better match queries to the right policy area. This often dramatically boosts retrieval relevance for structured docs. D. Increase the document chunk size If your chunks are too small, key details may be split across multiple chunks and never surface together in the top-k. Enlarging the chunk window (while still staying under your model’s context limit) lets each chunk carry more contiguous context, so the retriever can return more complete sections. Options B and E address text splitting or generation tuning but won’t solve retrieval gaps; option C (bigger embedding model) was already tried indirectly when swapping embedding LLMs and didn’t fix the core issue of missing context.