0 characters · 0 words · 0 paragraphs
Ready to chunk
Paste text, configure options, and click Split// break text into evenly sized chunks instantly
Break any text into evenly sized chunks by characters, words, or paragraphs. Copy or download each chunk instantly. Free, browser-based, no sign-up.
Ready to chunk
Paste text, configure options, and click SplitDrop any text into the input — articles, code, transcripts, or large documents.
Choose split mode (characters, words, paragraphs), set chunk size, and optionally add overlap.
Copy individual chunks with one click, or download all as a plain text file.
Text Chunker breaks any text into evenly sized pieces based on your chosen unit — characters, words, or paragraphs. It's especially useful for AI/LLM workflows where context windows have token limits, or anytime you need to process large text in smaller pieces.
Overlap lets consecutive chunks share content at their edges, which helps preserve context across chunk boundaries — a common technique in RAG (Retrieval-Augmented Generation) pipelines.
Overlap means consecutive chunks share a portion of their content. For example, with a chunk size of 100 words and overlap of 10, chunk 2 will start 10 words before chunk 1 ends. This preserves context at boundaries and is widely used in AI/RAG workflows.
Character mode splits by exact character count — precise but may cut mid-word. Word mode splits by word count — cleaner, natural-language boundaries. Paragraph mode splits on double newlines, keeping natural logical sections together regardless of their length.
No hard limit — everything runs in your browser. Very large texts (millions of characters) may slow down rendering since all chunks are displayed. For processing speed and convenience, most users work with texts under 500,000 characters.
Large language models have a maximum context window (e.g. 128k tokens). When you need to process a document longer than this window, you must break it into chunks and send each one separately. This tool automates that splitting process, with overlap support for RAG pipelines.
No. All processing happens entirely in your browser using JavaScript. Your text is never uploaded or transmitted anywhere — it stays on your device at all times.
Clicking Download All creates a plain .txt file with all chunks separated by a delimiter line showing each chunk's number and character/word count. It's easy to parse programmatically or open in any text editor.
A text chunker is a tool that divides a large body of text into smaller, more manageable pieces called "chunks." This process is fundamental in many areas of software development, data processing, natural language processing (NLP), and AI-powered applications. Instead of working with an unwieldy wall of text, you get a series of structured, equally-sized segments that can be processed, stored, transmitted, or fed into AI models individually.
Our browser-based text chunker supports three core splitting strategies — by characters, by words, and by paragraphs — each suited to different use cases. It also supports chunk overlap, a critical feature for AI and machine learning pipelines where context continuity across boundaries matters.
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There are many practical reasons why developers, writers, data scientists, and AI engineers need to split text:
Character-based chunking divides text at a precise character count. This produces the most uniform chunks in terms of raw size, making it ideal when working with systems that measure input by character count (such as SMS APIs or certain AI APIs that charge per character).
The downside is that character chunking doesn't respect word boundaries. A chunk might end mid-word, for example splitting "consti" and "tution" across two chunks. For human readability, word-based or paragraph-based chunking is usually preferable. However, for purely programmatic processing, character chunking's precision is a major advantage.
Word-based chunking splits text at word boundaries, producing chunks of a specified word count. This is the most natural strategy for English and most Western languages since words are the semantic units of meaning. A 200-word chunk is a coherent passage of prose, while a 200-character chunk may not be.
Word chunking is commonly used for:
Paragraph-based chunking splits text on paragraph boundaries (detected by double newlines). Rather than enforcing a strict size, it groups text into its natural logical units. You set how many paragraphs per chunk.
This mode preserves the author's intended structure better than either character or word splitting. A paragraph is usually a complete thought or argument, so paragraph chunks maintain semantic coherence. This is particularly useful for processing structured documents like legal texts, academic papers, or technical documentation, where each paragraph is a self-contained unit.
Chunk overlap is an advanced feature that causes consecutive chunks to share a portion of their content. For example, if you set a chunk size of 500 words with an overlap of 50 words, chunk 1 covers words 1–500, chunk 2 covers words 451–950, chunk 3 covers words 901–1400, and so on.
This technique originates in Retrieval-Augmented Generation (RAG) pipelines for AI systems. When a long document is split and stored in a vector database, queries retrieve the most relevant chunks. Without overlap, a piece of information that spans a chunk boundary might be missed — it appears in neither chunk's context. Overlap ensures that content at chunk boundaries is represented in at least two chunks, improving recall.
Overlap is also useful when summarizing chunked text: the overlap region provides context so the summarizer understands how each chunk connects to the previous one.
Retrieval-Augmented Generation (RAG) is a widely-used architecture in modern AI applications. Instead of fine-tuning a model on proprietary data, you store knowledge in a vector database, retrieve relevant chunks at query time, and inject them into the LLM's context. The quality of your chunking strategy directly affects the quality of your retrieval — and therefore the quality of your AI's answers.
Common best practices for AI chunking include:
Our tool gives you direct control over all these parameters, making it a practical utility for anyone building or experimenting with RAG systems without writing custom preprocessing code.
For most AI workflows, start with word mode, a chunk size of 200–300 words, and an overlap of 20–30 words. Observe the chunk count and adjust the size to get the number of chunks that makes sense for your use case.
For newsletter or content splitting, paragraph mode with a size of 2–3 paragraphs per chunk gives natural, readable sections. For precise API payload control, character mode is your best option — just be aware of the mid-word split behavior and post-process if needed.
After chunking, use the individual copy buttons to grab specific chunks, or Download All to get the full split document as a text file that you can import into a script or pipeline.