Smart Prompt Chunking Station
Automate text division patterns cleanly inside strict LLM window token limits.
Context window: 128,000 tokens
Tokens repeated between adjacent chunks
Paste text above and configure your chunking strategy.
About This Tool
The Smart Prompt Chunking Station solves one of the most common pain points in LLM workflows: feeding large documents into models that have strict context-window token limits. Rather than manually cutting text and guessing at token counts, this tool automates the entire division process using configurable chunking strategies tailored to your target model.
Choose from four splitting strategies — token-estimated splitting, sentence-aware splitting, paragraph-aware splitting, or a custom delimiter — and optionally inject a shared system-message prefix and task-instruction suffix into every chunk. The tool also supports configurable overlap between adjacent chunks, which is critical for RAG pipelines and sliding-window summarisation where you do not want context to be lost at chunk boundaries.
How to Use
- Select your target model from the preset list (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, etc.) or enter a custom token limit.
- Choose a chunking method: By Token Estimate divides by approximate token count; By Sentences keeps sentences intact; By Paragraphs keeps paragraph blocks together; Custom Delimiter splits on any string you define (e.g.
---or\n\n). - Set Chunk Size & Overlap using the sliders. Overlap repeats the last N tokens of each chunk into the start of the next — ideal for retrieval-augmented generation (RAG).
- Add Prefix / Suffix to inject a system message or task instruction that will wrap every chunk automatically.
- Paste your text and see chunks generated instantly. Expand any chunk to read it, copy individual chunks, or export all as a structured JSON array.
Common Use Cases & FAQ
A Quick Note
Token estimates are approximate and based on English-language GPT-family heuristics. All processing is 100% client-side — no text is sent to any server.
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