Late Chunking & Contextual Retrieval: Solving Loss
📖 Bản tiếng Việt (Vietnamese Edition) Prerequisite: Familiarity with the concepts introduced in Part 2 — Agentic Ingestion Multimodal. Review it first if the terminology in this part is unfamiliar. Part 3 — Late Chunking & Contextual Retrieval: Solving Chunk Boundary Loss Answer-first: Standard early chunking splits text prior to embedding, destroying long-range semantic dependencies and pronoun references across chunk boundaries. Late Chunking passes the full document through the Transformer encoder layer first, computing token-level contextual representations before applying mean pooling over chunk boundaries to boost retrieval precision by 27%. Late Chunking maintains full-document cross-attention states before pooling token spans, which when combined with two-tier Binary Quantization in Redis, delivers 27% higher retrieval precision and sub-15ms semantic cache hits. ...