"A hybrid of vector and term-based search is the most effective strategy for RAG pipelines that answer user questions about documentation. Both vector databases and Lucene-based search engines support this, but tuning the underlying algorithm is critical for optimal results. When the domain is complex enough, and the questions are sufficiently sophisticated and nuanced, similarity (which is what you get out of a document search) is not the same thing as relevance (which is what the LLM needs to answer the question)."
"Chunking refers to the process of breaking down content into smaller units when indexing documents for a database. The database search could miss the similarity if the chunks are too large or too small. The basis for chunking should differ depending on the knowledge domain and the type of content and media used to deliver it."
Hybrid vector and term-based retrieval delivers superior results for RAG pipelines answering documentation questions, provided the retrieval algorithm is carefully tuned. Similarity scores from document search do not necessarily equate to the relevance required for LLM answers when domains or queries are complex and nuanced. Chunk size must be chosen to suit the knowledge domain and content media, because overly large or small chunks reduce retrieval effectiveness. Different content types—diagrams, graphs, sample code, tables, and prose—require distinct indexing strategies. Context window constraints make it essential to include only the most relevant search results in prompts.
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