LLM Text Chunker & Token Splitter
Split long documents into equal-sized chunks with customizable word or token overlap for AI and RAG prompts.
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- Private in browser
- No signup
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LLM Text Chunker & Token Splitter
Divide long documents into equal-sized chunks with rolling overlap for RAG and AI prompts.
Document Input
Generated Chunks (4)
Large language models (LLMs) are deep learning algorithms capable of recognizing, summarizing, translating, predicting, and generating text and other content based on knowledge gained from massive datasets. Retrieval-Augmented Generation (RAG)
massive datasets. Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model by referencing an authoritative knowledge base outside of its training data sources before
its training data sources before generating a response. Chunking is a critical initial step in any RAG pipeline. When indexing documents into a vector database, documents must be partitioned into
documents must be partitioned into segments of appropriate token lengths so that embedding models can accurately capture topical semantics.
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About LLM Text Chunker & Token Splitter
Split long documents into equal-sized chunks with customizable word or token overlap for AI and RAG prompts. Use this free client-side tool on TheToolss with zero installation and complete privacy.
How to use LLM Text Chunker & Token Splitter
- Paste your long document or transcript into the source editor.
- Choose your chunking unit (Words or Characters) and target chunk size.
- Set your desired overlap (e.g. 10% or 50 words) to maintain semantic continuity.
- Copy individual chunks or export all segments as structured JSON.
Key features
- Chunking by Word Count, Character Count, or Paragraph boundaries
- Customizable sliding window overlap preventing context cutoff between chunks
- Chunk statistics: word count, estimated tokens, and character length per slice
- One-click JSON export array ready for OpenAI, Claude, or Pinecone ingest
- 100% client-side privacy ensuring confidential documents never leave your browser
Frequently asked questions
- What is text chunking in LLMs and RAG?
- Chunking is the process of breaking long texts into smaller, semantically coherent passages so they can fit inside LLM context limits and be indexed into vector databases.
- Why is chunk overlap important?
- Overlap ensures that sentences and thoughts at the edge of a chunk boundary are not severed in the middle of an important concept, maintaining high search relevance in RAG.
- How do words relate to LLM tokens?
- On average in English, 1 word is roughly 1.33 tokens (or 1,000 tokens ≈ 750 words).
- Can I export the chunks into JSON?
- Yes! The tool includes a "Copy All as JSON" option that creates a clean JSON string array of all chunk passages.
- Is my confidential text uploaded anywhere?
- No. All splitting algorithms run entirely in your local browser JavaScript memory.