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RAG Text Splitter

Visualize how your text is chunked for RAG applications. See overlap and context windows instantly.

Source Text

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Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language mod
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a large language model, so it references an authoritative knowledge base outside its training data s
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its training data sources before generating a response. Large Language Models (LLMs) are trained on
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LLMs) are trained on vast volumes of data and use billions of parameters to generate original output
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rate original output for tasks like answering questions, translating languages, and completing sente
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and completing sentences. RAG extends the already powerful capabilities of LLMs to specific domains
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to specific domains or an organization's internal knowledge base, all without the need to retrain th
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e need to retrain the model.

Why RAG Chunking Matters

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Context Window

LLMs have a limited context window. Breaking large documents into smaller chunks ensures the relevant information fits into the prompt without errors.

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The Overlap Trick

Never split strictly! Adding "overlap" ensures that context isn't lost if a sentence is cut in the middle. The highlighted yellow text above shows this safety buffer.

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Better Retrieval

Smaller, focused chunks (e.g., 500-1000 chars) are semantically denser. This makes Vector Search find the exact answer much more accurately than searching huge documents.

Mastering Text Splitting for RAG

In Retrieval-Augmented Generation (RAG), the quality of your retrieval is determined by the quality of your chunks. If your chunks are too large, you retrieve too much noise. If they are too small, you lose the semantic meaning needed to find the answer.

Common Chunking Strategies

  • Character Splitting: The simplest method (used in this tool). Splits text strictly by character count. Fast but can break sentences awkwardly.
  • Recursive Character Splitting: A smarter method (used by LangChain) that tries to split by paragraphs first, then sentences, then words, keeping semantic units together.
  • Semantic Chunking: An advanced method that uses embeddings to detect "topic shifts" and splits the text only when the topic changes.

How to Choose Chunk Size?

There is no one-size-fits-all answer, but here is a heuristic:

  • Fact Retrieval (Q&A): Use smaller chunks (256 - 512 tokens). You want precise facts.
  • Summarization: Use larger chunks (1024 - 2048 tokens). The model needs more context to understand the "big picture".
  • Code Search: Use function-level chunking. Never split a function in half.

Frequently Asked Questions

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