Semantic Chunking
A document-segmentation approach that tries to preserve topical and semantic boundaries instead of cutting text only by a fixed character or token count.
When a retrieval chunk ends in the middle of a sentence, paragraph, or technical explanation, its embedding may lose the context needed for a useful match. Semantic chunking uses structure such as headings, paragraphs, topic transitions, and sometimes embedding similarity to keep related material together.
Larger chunks are not automatically better: too much context can dilute the vector and bring unrelated material into the same result. Chunk policy should therefore be validated against retrieval metrics and real query distribution. FAISS index architecture for vector similarity search represents the next stage of that retrieval pipeline.