Sentence splitting, or sentence segmentation, identifies sentence boundaries and divides text into sentence-level units. In retrieval-augmented generation (RAG), it helps create chunks that preserve whole sentences, context, and meaning instead of cutting information arbitrarily. section 7.7.2
Choose the approach according to context, performance, and corpus structure:
- Use NLP-based splitting when question answering or generation depends on language nuance and coherent context. section 7.7.5
- Use fixed-length splitting when performance, scale, or limited computational resources matter more than preserving sentence boundaries. section 7.7.2
- Compare sentence splitting with token-based, semantic, hierarchical, punctuation-based, and topic-based chunking before selecting a production strategy. section 7.7.2
Sentence-aware RAG chunks
NLTK's sent_tokenize() uses a pretrained PunktSentenceTokenizer to detect boundaries. A generator version yields sentences incrementally, which can reduce memory use for large text. spaCy can detect sentences, count tokens, and combine complete sentences into chunks constrained by max_tokens, with optional overlap. In the comparison, spaCy produced four embeddings in 5.8 seconds, while NLTK produced 105 in 13.31 seconds; results depend on the implementation and corpus. section 7.7.5
