Why should I clean text before sending it to an LLM?

Raw text often contains HTML tags, invisible control characters, inconsistent whitespace, and non-standard Unicode that can confuse models or waste tokens. Cleaning the text ensures the model focuses on actual content rather than noise.

What does Unicode normalization (NFKC) do?

NFKC normalization converts characters to their canonical forms — for example, replacing fancy Unicode letters, ligatures, and compatibility characters with their standard equivalents. This prevents the model from seeing the same word represented differently.

Should I always lowercase text for LLMs?

Not always. Lowercasing reduces vocabulary size and can improve consistency, but modern LLMs handle mixed case well. Avoid lowercasing if you need to preserve proper nouns, acronyms, or case-sensitive identifiers.

AI Text Cleaner

Clean and sanitize text for LLM input by stripping HTML, normalizing Unicode, and collapsing whitespace.

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guide:chunking strategies use case:data cleaning

FAQ

Why should I clean text before sending it to an LLM?: Raw text often contains HTML tags, invisible control characters, inconsistent whitespace, and non-standard Unicode that can confuse models or waste tokens. Cleaning the text ensures the model focuses on actual content rather than noise.
What does Unicode normalization (NFKC) do?: NFKC normalization converts characters to their canonical forms — for example, replacing fancy Unicode letters, ligatures, and compatibility characters with their standard equivalents. This prevents the model from seeing the same word represented differently.
Should I always lowercase text for LLMs?: Not always. Lowercasing reduces vocabulary size and can improve consistency, but modern LLMs handle mixed case well. Avoid lowercasing if you need to preserve proper nouns, acronyms, or case-sensitive identifiers.

Prepare text for LLM input with a configurable cleaning pipeline. Toggle individual operations: strip HTML tags, normalize Unicode (NFKC), remove control characters, collapse whitespace, and lowercase. See exactly how many characters were removed at each step.