How We Achieved 91.94% Context Detection Accuracy Without Fine-Tuning

How We Achieved 91.94% Context Detection Accuracy Without Fine-Tuning The Problem When building Prompt Optimizer, we faced a critical challenge: how do you optimize prompts without knowing what the user is trying to do? A prompt for image generation needs different optimization than code generation. Visual prompts require parameter preservation (keeping --ar 16:9 intact) and rich descriptive language. Code prompts need syntax precision and structured output. One-size-fits-all optimization fails because it can't address context-specific needs. The traditional solution? Fine-tune a model on thousands of labeled examples. But fine-tuning is expensive, slow to update, and creates vendor lock-in. We needed something better: high-precision context detection without fine-tuning . The goal was ambitious: 90%+ accuracy using pattern-based detection that could run instantly in any MCP client. Our Approach We built a Precision Lock system - six specialized detection categories, each with custom p