Chunking Simulator

Visually compare document chunking strategies: Fixed Size, Semantic, and Sentence-based chunking.

How It Works

  1. Paste your document: Import a text or document you want to split.
  2. Adjust parameters: Modify chunk size and overlap percentage.
  3. Compare strategies: Visualize side by side the result of 3 chunking methods.

Frequently Asked Questions

What chunk size should I choose for my RAG?
Optimal size depends on your use case. For factual Q&A, 200-500 tokens. For document synthesis, 500-1000 tokens. For code, 100-300 tokens. Test multiple sizes with this tool.
What is the role of overlap?
Overlap preserves context between adjacent chunks. An overlap of 10-20% avoids cutting ideas in the middle. Too much overlap increases storage and can create redundancy in results.
Semantic vs fixed-size chunking: which to choose?
Semantic chunking preserves natural paragraphs and meaning, ideal for varied documents. Fixed-size is more predictable and fast, ideal for homogeneous content like code or logs.
How does chunking affect RAG quality?
Poor chunking degrades retrieval. Too small chunks lose context. Too large chunks dilute relevant information. Chunking is often the most underestimated optimization lever.
Can I combine multiple chunking strategies?
Yes, it's even recommended for mixed corpora. Use sentence chunking for FAQs, semantic for articles, and fixed-size for code. Ailog automatically handles this adaptation.
How many tokens per chunk for OpenAI ada-002?
ada-002 supports up to 8191 tokens but that's not optimal. Aim for 256-512 tokens per chunk for good balance between context and retrieval precision. Small chunk embeddings are more discriminating.
Ailog
Sign up

Chunk it

Visually compare document chunking strategies

Size300
Overlap50

Enter text to see chunks

Ailog optimizes chunking automatically.

Try it

How It Works

  1. 1

    Paste your document

    Import a text or document you want to split.

  2. 2

    Adjust parameters

    Modify chunk size and overlap percentage.

  3. 3

    Compare strategies

    Visualize side by side the result of 3 chunking methods.

More Tools

Test an AI Widget on Your Site

Enter your website URL and get a working AI chatbot in 30 seconds. Free, no signup required.

Test an Internal AI Chatbot

Create an AI knowledge base for your team in seconds. Free, no signup required.

AI Answer Generator

Get instant, accurate answers to any question. Free AI tool, no signup required.

RAG Quality

Evaluate your RAG response quality with RAGAS metrics

Embedding Cost

Compare embedding costs across major providers

Retrieval Tester

Test the retrieval quality of your RAG system

Frequently Asked Questions

Optimal size depends on your use case. For factual Q&A, 200-500 tokens. For document synthesis, 500-1000 tokens. For code, 100-300 tokens. Test multiple sizes with this tool.

Overlap preserves context between adjacent chunks. An overlap of 10-20% avoids cutting ideas in the middle. Too much overlap increases storage and can create redundancy in results.

Semantic chunking preserves natural paragraphs and meaning, ideal for varied documents. Fixed-size is more predictable and fast, ideal for homogeneous content like code or logs.

Poor chunking degrades retrieval. Too small chunks lose context. Too large chunks dilute relevant information. Chunking is often the most underestimated optimization lever.

Yes, it's even recommended for mixed corpora. Use sentence chunking for FAQs, semantic for articles, and fixed-size for code. Ailog automatically handles this adaptation.

ada-002 supports up to 8191 tokens but that's not optimal. Aim for 256-512 tokens per chunk for good balance between context and retrieval precision. Small chunk embeddings are more discriminating.

06 43 80 96 17Book a call