What order are preprocessing steps applied in?

Steps are applied in this order: (1) trim leading/trailing whitespace, (2) normalize typographic characters (smart quotes, dashes), (3) strip HTML tags, (4) collapse multiple spaces and newlines, (5) truncate to the selected model's context window at a word boundary.

How does truncation to context window work?

The tool estimates token count using a word-based approximation and cuts the text at the last word boundary before the model's context limit. This ensures the output will fit in the model's context without being cut mid-word.

Can I skip individual steps?

The pipeline runs all steps in sequence. For individual step control, use the dedicated tools: AI Text Cleaner for HTML/whitespace, AI Text Normalizer for typographic normalization, or AI Chunk Overlap for chunking long documents.

AI Input Preprocessor

Full preprocessing pipeline for LLM input: trim, normalize, strip HTML, collapse whitespace, and truncate to context window.

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ACOAI Chunk Overlap ToolNEW

Split text into token-sized chunks with configurable overlap for RAG and embedding pipelines.

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FAQ

What order are preprocessing steps applied in?: Steps are applied in this order: (1) trim leading/trailing whitespace, (2) normalize typographic characters (smart quotes, dashes), (3) strip HTML tags, (4) collapse multiple spaces and newlines, (5) truncate to the selected model's context window at a word boundary.
How does truncation to context window work?: The tool estimates token count using a word-based approximation and cuts the text at the last word boundary before the model's context limit. This ensures the output will fit in the model's context without being cut mid-word.
Can I skip individual steps?: The pipeline runs all steps in sequence. For individual step control, use the dedicated tools: AI Text Cleaner for HTML/whitespace, AI Text Normalizer for typographic normalization, or AI Chunk Overlap for chunking long documents.

Run text through a complete preprocessing pipeline before sending to an LLM API. Steps: trim whitespace, normalize typographic characters, strip HTML tags, collapse whitespace, then truncate at word boundaries to fit the selected model's context window. Shows token counts at each pipeline stage as a vertical timeline.