Chunking is the unsung hero of retrieval-augmented generation—the invisible architecture that determines whether your AI application returns brilliant, contextually relevant responses or frustrating, fragmented nonsense. When you split a 50-page technical document into pieces, every decision you make about where to cut, how much to overlap, and what metadata to attach cascades through your entire system, affecting embedding quality, retrieval precision, and ultimately user satisfaction.
123456789101112import nltk from typing import List, Tuple def semantic_chunk( text: str, target_size: int = 512, overlap_sentences: int = 2 ) -> List[Tuple[str, dict]]: """Chunk text at sentence boundaries with configurable overlap.""" nltk.download('punkt', quiet=True) sentences = nltk.sent_tokenize(text)