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Retrieval-Augmented Generation (RAG)

Document Chunking & Overlap

Chunking splits long documents into smaller pieces. Overlap repeats a few words between chunks so important meaning is not lost at the boundary.

This is a simple learning demo. Real RAG systems may use more advanced chunking methods.

Interactive Playground

Real RAG systems split text different ways — by word count, character length, sentence, or paragraph — depending on the content.

Live Visualization

Document Timeline

Chunks

Without Overlap vs With Overlap

🚫 Without Overlap

0
Chunks

Storage: Uses less storage, but nearby meaning may be split.

Context: Words at chunk boundaries can lose their connection to nearby meaning.

🔗 With Overlap

0
Chunks

Storage: Uses more storage, but keeps more context between chunks.

Context: Repeated words at the boundary help preserve meaning across chunks.

Search Demo

Try the default question below — it sits right on a chunk boundary, so overlap makes the difference.

Statistics

0
Total Words
15
Chunk Size
3
Overlap Size
0
Chunks
0
Avg Words / Chunk
0
Extra Repeated
—
Phrase Found With Overlap

How It Works

📄
Long
Document
→
✂️
Split into
Chunks
→
🔗
Add
Overlap
→
🧩
Preserve
Context
→
🎯
Better
Retrieval
Overlap keeps context. A little overlap helps chunks keep meaning that would otherwise be cut off at the boundary.
Smaller chunks = more, narrower pieces. Larger chunks mean fewer, broader pieces, try dragging the sliders to see the trade-off.
This demo splits by word count. Real RAG systems often chunk by sentences or paragraphs for more natural boundaries.
💡
Key Takeaway

Overlap repeats a small part of the previous chunk so important context is less likely to be lost.

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