humanize-draft

To detect chatbot artifacts in draft writing, you can scan the text against a fixed set of 15 high-confidence LLM patterns. This process identifies phrases like "as a large language model" to generate a structured feedback file with a humanization score and highlighted issues.

Common LLM clichés that reduce clarity include phrases like "As a large language model", "I hope this helps!", and common hedges. Identifying these chatbot artifacts helps tighten drafts for professional or academic writing by surfacing machine authorship indicators.

You get a humanization score by instructing the tool to scan a draft file and generate a humanization report. This report applies pattern matching against 15 specific phrases to output a structured feedback file containing the score and highlighted text issues.

Yes, you can use this to check sections or notes instead of a full draft. The pattern matching applies to any drafts, sections, or notes where detecting phrases like "I think" improves clarity and credibility across professional or academic writing contexts.

Draft analysis for LLM patterns requires no specific dependencies. It applies pattern matching against a fixed set of 15 phrases directly to the provided text, outputting a structured feedback file with a humanization score and highlighted issues.

The best way to flag AI-isms for professional writing review is scanning the text for 15 high-confidence LLM patterns. This surfaces machine authorship indicators like common hedges, generating a structured feedback file to tighten content before review.