1. Routing exists to protect quality and budget at the same time
Sending every request to the same model is easy but expensive and often unnecessary. Routing by intent helps the system reserve stronger models for tasks that actually need them.
2. The routing taxonomy has to be simple enough to operate
A useful taxonomy might separate factual lookup, extraction, structured transformation, policy-sensitive reasoning, and long-form synthesis. If the categories are too abstract, the team cannot debug misroutes effectively.
3. Difficulty and risk should be evaluated separately
Some requests are computationally simple but high-risk because of policy or customer impact. Others are low-risk but long and expensive. Keeping those dimensions separate improves routing decisions.
4. Fallbacks need explicit rules
When the first model underperforms, the system should know whether to retry, escalate to a stronger model, or stop and ask for clarification. Without that logic, routing often increases hidden cost instead of reducing it.
5. Weekly review reveals where the routing is wrong
Teams should inspect false savings, quality drops, and expensive escalations regularly. That is how routing evolves from a static rule table into a real operating layer.
Practical Checklist
- Define a small routing taxonomy based on intent and task class.
- Score difficulty and risk separately when choosing a model.
- Document explicit fallback and escalation rules.
References
- OpenAI, Text generation guide
Useful for understanding model behavior and response-class assumptions.
- OpenAI API pricing
Relevant for mapping routing decisions to cost tradeoffs.
- Anthropic, Choosing the right model
A helpful reference for the broader routing mindset across model classes.