AI utility · context planning

Split long documents into prompt-ready context chunks.

Prefer paragraph and sentence boundaries while reserving room for instructions, the answer, and overlap between chunks.

Token counts are transparent estimates. Verify production inputs with the target model's tokenizer.

A chunk summary and copyable sections will appear here.

Token CalculatorRAG Cost CalculatorOCR

How to use the chunk plan

The chunker prefers paragraph, line, and sentence endings. It falls back to a hard character boundary only when needed. Overlap repeats the end of the previous chunk to preserve cross-boundary context, but it also increases cost.

This estimate does not call a model or provider tokenizer. CJK text, code, tables, emoji, and unusual punctuation can differ materially. Budget each chunk together with the system prompt, chat history, tool results, and expected answer, then validate retrieval and answer quality on real tasks.