RAG Chunking Calculator
Estimate chunk counts, overlap waste, vector storage size, and embedding cost for your RAG knowledge base. Get recommended chunk size and overlap for your document type and chunking strategy.
Configure Your Corpus
Document Type
Corpus Size
~2,000,000 total raw tokens (5,000 pages × 400 tokens/page)
Chunking Strategy
Best for: General-purpose mixed corpus, default starting point
Chunk Configuration
Effective stride: 435 tokens · overlap: 77 tokens/chunk
Embedding Model
Retrieval Settings
Configure your corpus and click Calculate
Chunk count, storage size, embedding cost, and chunking recommendation will appear here
RAG Chunking Best Practices
- Chunk size is the most impactful RAG parameter. Too large: retrieval returns irrelevant noise alongside relevant content. Too small: chunks lose context and embeddings become less meaningful. Start at 256–512 tokens and tune from there.
- Overlap prevents boundary information loss — a fact split across two chunks will fail retrieval without overlap. 10–20% is typical; above 30% wastes storage without meaningful quality gains.
- Never exceed your embedding model's token limit. Exceeding it causes silent truncation — the tail of the chunk is embedded without its text, corrupting the vector.
- Smaller chunks = better precision, larger chunks = better recall. For high-stakes retrieval (medical, legal, compliance), bias toward smaller chunks and higher top-k. For conversational RAG, larger chunks reduce hallucination by providing more context.
- Late chunking and semantic chunking improve quality but increase ingestion costby 5–20×. Reserve them for high-value, relatively static knowledge bases.