Model Routing
Model routing chooses which model or model path handles each request. The choice follows clear rules for quality, skill, cost, delay, or safety policy.
Another example
A platform uses request types, test scores, prices, and delay goals to choose a model. Simple summaries go to a smaller model, while high-risk cases use a reviewed path. Random load balancing with no skill rules is not model routing.
Main parts
- 01request classifier
- 02routing policy
- 03fallback path
Use it when
Use this term when measurable limits guide a request to one of several model paths.
Do not use it when
Do not route only by model name or price without a fallback, tests, and policy checks.
Name used in code
request features → routing policy → model Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Model Routing to make AI behavior measurable and tied to evidence. User context: A platform uses request types, test scores, prices, and delay goals to choose a model. Simple summaries go to a smaller model, while high-risk cases use a reviewed path. Random load balancing with no skill rules is not model routing. AI method or concept: Model Routing. Why it fits: Use this term when measurable limits guide a request to one of several model paths. Do not use it when: Do not route only by model name or price without a fallback, tests, and policy checks. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): No single official standard controls the whole term. Its meaning may change across teams, platforms, or frameworks. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Model Routing. Definition: Model routing chooses which model or model path handles each request. The choice follows clear rules for quality, skill, cost, delay, or safety policy. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.