The Struggle: Why Generic Optimization Fails I spent six months debugging why our token reduction pipeline was destroying prompt intent. We had a solid optimization engine that cut tokens by 35%, but the outputs were drifting. A code generation prompt would lose its security constraints. A creative writing prompt would become mechanical. A data analysis prompt would hallucinate. The problem wasn't the optimization logic. It was that we were treating all prompts the same. I realized we were applying readability optimizations to security-critical code prompts and logic-preservation techniques to creative tasks. We needed to know what we were optimizing before we optimized it. That's when I started building the context detection layer. The Real Problem: Prompts Aren't Interchangeable Most prompt optimization tools work like generic code minifiers. They strip whitespace, consolidate instructions, remove "redundant" phrases. This works fine for reducing file size.…