RAG Chunking Calculator

RAG (Retrieval-Augmented Generation) splits documents into chunks before embedding and storing them in a vector database. When a query arrives, similar chunks are retrieved and passed to the LLM as context. Chunk size directly affects retrieval quality: too small and you lose context; too large and you dilute relevance and waste tokens.

For general text: 512–1024 tokens works well. For technical docs with dense code: 256–512. For conversational transcripts: 128–256. For long-form articles: 1024–2048. The key is that each chunk should be semantically complete — a full paragraph, code block, or logical section — not an arbitrary cut.

Overlap of 10–20% of chunk size prevents context loss at boundaries. For a 512-token chunk, 50–100 tokens of overlap is typical. Higher overlap increases storage and retrieval cost but reduces the chance of splitting important context. For factual documents, lower overlap is fine; for narrative or code, higher overlap helps.

Token chunking (tiktoken/BPE) gives exact LLM token counts and aligns with model limits, but is slower. Character chunking is faster and language-agnostic but less precise. Sentence-aware chunking (NLTK, spaCy) produces more semantically coherent chunks but is more complex. For production RAG, token-aware chunking is recommended to avoid hitting context limits.

Embeddings represent the semantic meaning of the entire chunk as a single vector. Smaller chunks produce more specific, high-precision embeddings — good for exact fact retrieval. Larger chunks produce broader, more contextual embeddings — good for concept/topic retrieval. For hybrid search (dense + sparse), medium chunks (512–768 tokens) often perform best.