What is prompt chunking?

Prompt chunking (or text splitting) divides a long document into smaller pieces that each fit within a model's context window. This is useful when processing large texts through AI models that have token limits.

What does chunk overlap do?

Overlap repeats the last N tokens of one chunk at the start of the next. This preserves context across chunk boundaries so the model doesn't lose important information that spans two chunks.

How is the chunk size calculated?

This tool uses the same word-based token estimation as the AI Token Counter (~1.3 tokens per word). Results are approximate — leave a safety margin below your model's actual context limit.

AI Prompt Splitter

Split long prompts into chunks that fit within model context windows.

Text to Split0 tokens total

Chunk Size2,000 tokens

5008,000

Overlap100 tokens

0500

Paste text above to split it into chunks

Related Tools

PBDAI Prompt BuilderNEW

Build structured AI prompts with role, task, context, and output format fields.

FMTAI Prompt FormatterNEW

Clean and format AI prompts by removing invisible characters and normalizing whitespace.

TKNAI Token CounterNEW

Count tokens for GPT, Claude, Gemini, and LLaMA models.

LENAI Prompt Length OptimizerNEW

Check if your prompt fits within a model context window and get compression tips.

Learn More

guide:chunking strategies guide:context window management

FAQ

What is prompt chunking?: Prompt chunking (or text splitting) divides a long document into smaller pieces that each fit within a model's context window. This is useful when processing large texts through AI models that have token limits.
What does chunk overlap do?: Overlap repeats the last N tokens of one chunk at the start of the next. This preserves context across chunk boundaries so the model doesn't lose important information that spans two chunks.
How is the chunk size calculated?: This tool uses the same word-based token estimation as the AI Token Counter (~1.3 tokens per word). Results are approximate — leave a safety margin below your model's actual context limit.

Split long AI prompts or documents into chunks that fit within context windows. Set the chunk size in tokens and an overlap amount to preserve context between chunks. Chunks are split at word boundaries and each chunk shows its token count.