Six intermediate technical questions evaluating core Retrieval-Augmented Generation (RAG) and vector retrieval mechanics: semantic document chunking, dense vector space projection via bi-encoders, HNSW graph probability layer assignment, Reciprocal Rank Fusion (RRF) for hybrid search, cross-encoder re-ranking dynamics, and chunk boundary truncation failure modes.
0 / 6
In Retrieval-Augmented Generation (RAG) pipelines, why does semantic document chunking based on embedding distance thresholds outperform fixed-size token window chunking?