RAG CHUNKING CALCULATOR

Optimize Your Document Chunks

Enhance your Retrieval-Augmented Generation pipeline by experimenting with different chunking strategies.

Input Text

Paste your document content below (max 1000 words)

0/1000 words

For best results, use well-structured text with complete sentences.

Words per chunk

50 200 500

Larger chunks provide more context but may be less precise.

Words between chunks

0 20 100

Higher overlap reduces context loss but increases redundancy.

Current configuration

Total Chunks:

Avg Chunk Size:

Redundancy:

Chunk

Overlap

Preview how your document will be split based on current settings

Paste your text above and adjust the sliders to see how it will be divided into chunks

0 of 0 chunks displayed

Small Chunks (50-150 words)

Best for precise retrieval of facts or when working with short documents like FAQs or product descriptions. Use overlap of 10-20 words.

Medium Chunks (150-300 words)

Good balance for most documents, providing enough context while maintaining retrieval precision. Use overlap of 20-40 words.

Large Chunks (300-500 words)

Useful for complex topics requiring more context, like research papers or technical documentation. Use overlap of 40-60 words.

Advanced Strategies

Consider semantic chunking for complex documents, or hybrid approaches combining fixed-size chunks with paragraph boundaries.

RAG Chunking Calculator v1.0 | Designed for AI pipeline optimization

Use this tool to experiment with different chunking strategies for your Retrieval-Augmented Generation systems