What This Tool Does
The Text Chunker splits any block of text into numbered segments of a configurable size. You can split by characters (respecting word boundaries), words, sentences, or paragraphs. An optional overlap setting makes each chunk begin slightly before the previous one ended — a technique widely used in AI retrieval pipelines to ensure that information at chunk boundaries is not lost. Each chunk shows its character count, word count, and a one-click copy button.
How to Use
- Paste or type the text you want to split into the input box, or click Sample to load an example.
- Choose a Split by mode: Characters, Words, Sentences, or Paragraphs.
- Set the Chunk size — for example, 500 characters or 100 words per chunk.
- Optionally set an Overlap value so consecutive chunks share some content at their boundaries.
- Scroll through the generated chunks and click Copy on any individual chunk to copy it to the clipboard.
Frequently Asked Questions
What is text chunking?
Text chunking is the process of dividing a long document into smaller, manageable pieces called chunks. Each chunk is a self-contained passage of text that can be processed, stored, or transmitted independently. Chunking is a foundational step in many text-processing workflows, from feeding documents into search indexes to preparing content for AI models that have a maximum input size.
Why does text chunking matter for AI and LLMs?
Large language models have a fixed context window — a maximum number of tokens they can process in a single request. When a document is longer than that limit, it must be split into chunks before being sent to the model. In retrieval-augmented generation (RAG) pipelines, chunks are embedded into vectors and stored in a vector database; the most relevant chunks are retrieved at query time and injected into the LLM prompt. The quality of chunking directly affects retrieval accuracy and the coherence of AI responses.
What is overlap, and when should I use it?
Overlap means that each chunk starts a few units (characters, words, etc.) before the end of the previous chunk, so consecutive chunks share a small window of content. This prevents important information that falls at a chunk boundary from being split across two non-overlapping segments where neither retains the full context. A typical overlap for character-based chunking is 50–100 characters; for word-based chunking, 10–20 words is common. Use overlap when semantic continuity between chunks is important, such as in RAG or summarization pipelines.
Which chunking mode should I choose?
Choose Characters when you need precise control over chunk byte size, such as fitting within a token budget. Choose Words when you want semantically whole units and a consistent approximate length. Choose Sentences when each chunk should contain complete thoughts — good for summarization. Choose Paragraphs when your document has natural section breaks and you want each chunk to represent a coherent topic. Character chunking with word-boundary respect is the most common choice for general-purpose LLM pipelines.