Teaching an AI to Pick Its Own Brain: Building Adaptive Model Routing
The article describes the development of an adaptive model routing system for an AI chatbot that selects the appropriate model tier based on task type rather than perceived difficulty. Traditional approaches like keyword matching, LLM-as-judge, and external routers failed due to language limitations, cognitive biases, and maintenance complexity. By classifying user queries into eight objective task categories, the system efficiently routes requests to cheap, medium, or strong models while maintaining performance and reducing costs.
- ▪The AI routing system was designed to handle multilingual conversations, primarily in Chinese, which ruled out English-biased routing models.
- ▪Classifying tasks by type—such as coding, casual chat, or research—proved more reliable than assessing prompt difficulty, avoiding the Dunning-Kruger effect in smaller models.
- ▪The final system uses a decision tree with eight categories and routes queries to appropriate model tiers, defaulting to medium for uncertain cases.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3899908) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Wavebro Posted on May 17 Teaching an AI to Pick Its Own Brain: Building Adaptive Model Routing #ai #claudecode #bots #devjournal Part 2 of the crab-bot series. If you missed Part 1, start here. The Problem Nobody Talks About Every AI chatbot has a dirty secret. It doesn't matter if you're asking "what time is it in Tokyo" or "redesign our entire microservice architecture to handle 10 million concurrent users." The model you get is the same model. Maximum horsepower. Every. Single.
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