RAG Chunk Size Calculator

Document Profile

Recommendations

Chunk Size
461
tokens
Overlap
46
tokens
Strategy
recursive
chunking

Reasoning

  • - General text: balanced 512-token chunks with recursive splitting.
  • - Factual lookups work well with focused, smaller chunks.
  • - Final chunk size: 461 tokens with 46 token overlap (10%).
  • - Token budget: 2,305 tokens for 5 chunks.

Token Budget Breakdown

Remaining
0128,000 tokens
System Prompt: 1,000
RAG Chunks: 2,305
User Query: 200
Response Budget: 2,000
Remaining: 122,495

About RAG Chunk Size Calculator

This tool uses a rule-based decision tree to recommend optimal chunk sizes, overlap, and splitting strategies based on your document type, query patterns, and embedding model constraints. The token budget visualizer shows how your context window is allocated across system prompt, retrieved chunks, query, and response.

What This Tool Does

RAG Chunk Size Calculator is built for deterministic developer and agent workflows.

Calculate optimal chunk size and overlap for RAG pipelines based on document type and embedding model.

Use How to Use for execution steps and FAQ for constraints, policies, and edge cases.

Last updated:

This tool is provided as-is for convenience. Output should be verified before use in any production or critical context.

Agent Invocation

Best Path For Builders

Browser workflow

Runs instantly in the browser with private local processing and copy/export-ready output.

Browser Workflow

This tool is optimized for instant in-browser execution with local data handling. Run it here and copy/export the output directly.

/rag-chunk-calculator/

For automation planning, fetch the canonical contract at /api/tool/rag-chunk-calculator.json.

How to Use RAG Chunk Size Calculator

  1. 1

    Determine optimal chunk size for document indexing

    Input: document length (e.g., 50,000 tokens), desired chunk size (512 tokens). The tool calculates number of chunks and suggests overlap (10-20%). Prevents semantic breaks mid-sentence.

  2. 2

    Calculate overlap for sliding window retrieval

    If using 1024-token chunks with 20% overlap, you get 205 token overlap. The tool shows what that means: context from previous chunk will be repeated. Good for maintaining coherence across retrieval boundaries.

  3. 3

    Optimize chunk size for LLM context windows

    If your LLM has 4K context, and you want to fit 3 chunks + query + system prompt (500 tokens total), max chunk size = ~1100 tokens. The calculator helps you stay within limits.

  4. 4

    Balance retrieval recall vs latency

    Larger chunks = fewer chunks to embed/retrieve (fast), but lower relevance precision. Smaller chunks = more to search (slow), but exact matches. The calculator shows trade-offs for your document size.

  5. 5

    Validate chunk strategy for multi-language documents

    Non-English text may tokenize differently. Calculate chunks for each language separately (e.g., Chinese is more token-dense than English). Ensures balanced chunk sizes across languages.

Frequently Asked Questions

What is the best chunk size for RAG?
It depends on your document type and query pattern. Technical docs typically work best at 256-512 tokens, conversational data at 128-256 tokens, and legal/research documents at 512-1024 tokens. This calculator provides specific recommendations.
What is chunk overlap and why does it matter?
Overlap is the number of tokens shared between adjacent chunks. It prevents information loss at chunk boundaries, where important context might be split. Typical overlap is 10-20% of chunk size.
Which chunking strategy should I use?
Recursive character splitting works well for most cases. Semantic chunking is better for documents with varied structure. Sentence-based works for FAQ and Q&A content. Paragraph-based is ideal for well-structured documents.
How many chunks should I retrieve (top-k)?
Start with 3-5 for factual lookup, 5-10 for summarization, and 8-15 for multi-hop reasoning. More chunks provide more context but increase cost and potential noise.
Does chunk size affect embedding quality?
Yes. Chunks that are too small lose context, making the embedding less meaningful. Chunks that are too large dilute the semantic signal. This calculator helps find the optimal balance for your use case.