Guide1 min readUpdated July 29, 2026

How to split long text for AI prompts

Short answer

Split long text by meaning, not just by character count. Keep chunks under your token budget, add a small overlap from the previous chunk, and label each chunk so the model can keep the order straight.

Why chunking helps

Language models have context limits. If your source material is too long, splitting it into smaller chunks makes review, summarization, extraction, and rewriting workflows easier to control.

Estimate the token budgetLeave room for the task instructions and the model's answer, not just the source text.
Split into chunksUse a max-token target that fits your workflow and keeps each chunk easy to inspect.
Add light overlapRepeat a small ending section from the previous chunk so the next prompt keeps nearby context.
Process in orderLabel chunks clearly and ask for a per-chunk result before combining the answers.

Try it: AI Text ChunkerSplit long text into estimated-token chunks with optional overlap locally.Open tool

Overlap is a tradeoffMore overlap preserves context but repeats tokens. Use enough to carry meaning across chunk boundaries, not a large duplicate block.

References

What are tokens and how to count them — OpenAI

Questions

How much overlap should I use?

Start with a small overlap, such as 5-10% of the chunk size. Increase it only when chunk boundaries cut through important context.

Are estimated chunks exact for every model?

No. Exact counts depend on the model tokenizer. Use estimates for planning and verify important limits with the provider's tokenizer when needed.

Does the text chunker upload my document?

No. Chunking and token estimation run locally in your browser tab.