AI / A CONCEPT NOTE
Model Routing
sending each prompt to the best model for the job
Overview · mechanism
pitfall · examples
01 / THE SHORT VERSION
The idea in a few sentences.
Model routing is the practice of dynamically selecting which LLM handles a given request based on criteria like complexity, cost, latency, or domain. A lightweight classifier (or simple heuristics) analyzes the incoming prompt and routes it — 'What time is it?' goes to a cheap, fast model; 'Debug this distributed system deadlock' goes to the most capable one. This optimizes spend and response quality simultaneously.
02 / FOLLOW THE MECHANISM
How a model router decides
User prompt
arrives at the routing layer: 'Explain the CAP theorem with examples.'
Classifier
evaluates complexity, token count, and domain tags — rates this as 'medium complexity, technical.'
Routing rules
match the classification to a model tier: simple → GPT-4o-mini, medium → Claude 3.5 Sonnet, hard → GPT-4o or Opus.
Selected model
receives the prompt and generates the response. The router logs which model was used, latency, and cost for optimization.
04 / COMMAND NOTES
Read the command, then the result.
Inspect the flags and arguments before trying an example. Snippets can need local setup, replacement values, or resources in your own environment.
use LiteLLM's router for multi-model routing
python -c "from litellm import Router; router = Router(model_list=[...]); router.completion(model='gpt-4o', messages=[...])"The ellipsis omits required code or values. This sketch is not runnable as written.
05 / CHECK YOURSELF
Could you explain Model Routing to a teammate?
Try it out loud in two sentences: what it is, and the one detail that changes the picture. If you stall, the gap is the part to reread.
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