Model Routing Classifier Prompt
A cheap-model prompt that labels request difficulty so it can be routed to the right model.
/ quick answer
Run this on a small, fast model before the main call. A cheap-model prompt that labels request difficulty so it can be routed to the right model.
Classify the user request by the capability needed to answer it well.
Return JSON: {"route": "easy" | "medium" | "hard", "reason": string}
easy = lookup, formatting, short factual reply, simple classification
medium = summarising or drafting from provided context, light multi-step
hard = multi-step reasoning, code changes, planning, ambiguous goals, math
If unsure between two levels, choose the higher one.
Request:
"""{{request}}"""{"route": "hard", "reason": "Asks for a database migration plan with rollback steps."}/ frequently asked
What does the Model Routing Classifier Prompt prompt do?
Run this on a small, fast model before the main call.
Which AI models work with this prompt?
It is model-agnostic: it works with any capable general model. Replace the bracketed variables with your own context before running it.
How do I use this prompt?
1) Copy the prompt with the copy button. 2) Replace every bracketed variable with your real details. 3) Paste it into your AI assistant. 4) Compare the answer with the example output and ask for changes if needed.
What output should I expect?
{"route": "hard", "reason": "Asks for a database migration plan with rollback steps."}.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →LLM Gateway
A proxy between your app and model providers that centralises keys, routing, fallbacks, logging and spend limits.
- →Small Language Model (SLM)
A language model with roughly 1–15B parameters, small enough to run on a laptop, phone or single GPU while handling narrow tasks well.
- →Prompt Caching
Reusing the processed form of a repeated prompt prefix so later requests are cheaper and faster.
- →Semantic Cache
A cache that returns a stored LLM answer when a new question means the same thing as an earlier one, even if worded differently.
Related workflows
Turn this into a repeatable process.
- →Route Requests Between Models
Send each request to the cheapest model that can handle it, escalating only when needed.
- →Cut LLM Inference Costs
A step-by-step method to reduce model spend without degrading output quality.
Related tool stacks
The tools that run it in production.
- →LLM Cost Optimization Stack
The layers that keep AI spend predictable as usage grows.
Related prompts
Reusable prompts for this job.
- →Red Team Attack Plan Prompt
Generates a tailored list of adversarial tests for your own AI feature.
- →AI System Threat Model Prompt
Produces a concrete threat model for an AI system with tool access.
- →Prompt Improver Prompt
Iterate a weak prompt into a strong one using best practices.
- →Strategic Analyst Prompt
Turn a business situation into a McKinsey-style strategic memo.
Related use cases
How people apply it, and what came out.
- →SaaS Cuts AI Bill With Routing
Illustrative example: routing easy requests to a small model reduces spend while holding quality steady.
Comparisons & alternatives
Pick between the options.
- →Reasoning Model vs Standard LLM
Reasoning models are more accurate on multi-step problems but slower and pricier; standard calls suit simple, fast tasks.