Why Automatic Prompt Classification Beats Manual Routing Rules

Why Automatic Prompt Classification Beats Manual Routing Rules Disclaimer: I'm the author of NadirClaw, the tool discussed below. Most LLM cost optimization tools ask you to write routing rules by hand. Config files. If-then statements. "Route this to GPT-5, that to Haiku." I tried that. It sucked. Here's why automatic classification wins, and what I learned building NadirClaw after ditching the config-file approach. The Config File Trap The typical manual routing setup looks like this: routes : - pattern : " translate.*" model : " gpt-5-mini" - pattern : " .*code.*" model : " claude-sonnet-4" - pattern : " .*complex.*" model : " gpt-5" - default : " gpt-5-mini" Seems clean. But three things kill it: 1. You can't predict prompts. Your coding assistant might send: "Refactor this function to handle edge cases better" Does that match .*code.* ? Sure. But is it simple enough for a cheap model? Maybe. Maybe not. The regex has no idea. 2. Maintenance nightmare. Every new use case needs a new