Your prompts fail in predictable ways. I’m building a regex NLP system that catches those patterns and fixes them in milliseconds—before the AI ever sees them

This system uses regex pattern matching to instantly detect your prompt’s intent by scanning for keyword signatures like “summarize,” “compare,” or “translate”—classifying it into one of eight categories without any machine learning. The system simultaneously flags ambiguity by identifying vague markers like “this,” “that,” or “make it better” that would confuse AI models, while also analyzing tone through urgency indicators. Based on these detections, heuristic rules automatically inject structured improvements—adding expert role context, intent-specific output formats (tables for comparisons, JSON for extractions), and safety guardrails against hallucinations. A weighted scoring algorithm evaluates the enhanced prompt across six dimensions (length, clarity, role, format, tone, ambiguity) and assigns a quality rating from 0-10, mapped to weak/moderate/strong classifications. The entire pipeline executes client-side in under 100 milliseconds with zero dependencies—just vanilla JavaScript regex operations and string transformations, making it faster and more transparent than ML-based alternatives. I am launching it soon as a blazing fast, privacy first prompt enhancer. Let me know if you want a free forever user account.