Classification & Routing

Intent classification is the backbone of most real AI systems. Learn to build query routers that send different types of inputs to specialized handlers — with confidence thresholds, fallback strategies, and hybrid fast-path/LLM architectures.

Quick Reference

→Intent classification is the first step in nearly every production AI system — get it right and everything downstream improves
→Use structured output (JSON mode or function calling) for reliable classification labels
→Set confidence thresholds: high confidence → auto-route, medium → fast model, low → human review
→Hybrid routing: keyword/regex for common intents, LLM for ambiguous or novel queries
→Always log classification decisions with confidence scores for later analysis and fine-tuning
→Multi-label classification (query can have multiple intents) is common in production — plan for it

Why Classification Is the Foundation

Most production AI systems are not a single monolithic LLM call. They are a routing layer that classifies the input and sends it to the right handler. A customer support bot routes billing questions to the billing agent, technical questions to the troubleshooting agent, and sales questions to the sales flow. A coding assistant classifies whether the user wants to edit code, explain code, generate tests, or fix a bug — each handler has different context, tools, and prompts.

Classification Accuracy Is a Multiplier

If your classifier is 90% accurate, every downstream handler starts with a 10% handicap — it receives queries it was not designed for. Improving classification accuracy from 90% to 98% often has a bigger impact on overall system quality than improving any individual handler.

▸Routing reduces latency: specialized handlers use shorter prompts and smaller context windows
▸Routing reduces cost: simple queries go to cheap models, complex queries go to expensive ones
▸Routing improves quality: each handler is optimized for its specific task
▸Routing enables specialization: different tools, different system prompts, different few-shot examples per intent

LLM Classification Strategies

There are multiple ways to get an LLM to classify text. The choice depends on your accuracy requirements, latency budget, and whether you need confidence scores.

Confidence-Based Routing

In production, you never want to blindly trust a classifier. Confidence-based routing lets you handle different confidence levels differently: high confidence goes straight to the handler, medium confidence gets a second opinion, and low confidence routes to a human.

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