How do I route different prompts to different models in one app?

A common way is to add a router layer in your app that decides which model to call based on the prompt, then sends the request to that model.

Basic pattern

1. Receive the user prompt
2. Classify the prompt by intent, complexity, or task type
3. Choose a model based on rules or a lightweight classifier
4. Send the prompt to the selected model
5. Return the result

Common routing strategies

1) Rule-based routing

Use simple if/else logic.

Example:

• Code questions → GPT-4.1 / coding model
• Short factual Q&A → cheaper fast model
• Creative writing → larger general model

def route_prompt(prompt):
    p = prompt.lower()

    if any(k in p for k in ["bug", "error", "stack trace", "code"]):
        return "coding-model"
    elif len(prompt) < 200:
        return "fast-model"
    else:
        return "general-model"

2) Classifier-based routing

Use a small model or custom classifier to label the prompt:

• code
• math
• summarization
• support
• creative

Then map labels to models.

This is better when rules get messy.

3) Confidence-based routing

Ask a small model to estimate:

• task type
• complexity
• whether a bigger model is needed

Example:

• If confidence is high and task is simple → cheap model
• If uncertain or complex → stronger model

4) Multi-stage routing

You can do:

• Step 1: classify intent
• Step 2: pick model
• Step 3: optionally verify output with another model

Useful for enterprise apps, support bots, or safety-critical flows.

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Practical architecture

User prompt
   ↓
Router / classifier
   ↓
Model selection
   ↓
Selected LLM API
   ↓
Response

You can implement the router as:

• simple app logic
• a dedicated microservice
• an LLM-based router
• a workflow engine

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Example in pseudocode

def handle_request(prompt):
    route = classify_prompt(prompt)

    if route == "code":
        model = "gpt-4.1"
    elif route == "summarize":
        model = "cheap-fast-model"
    elif route == "creative":
        model = "large-general-model"
    else:
        model = "default-model"

    return call_model(model, prompt)

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Tips

• Keep the router cheap and fast
• Log routing decisions so you can improve them
• Measure cost, latency, and quality
• Have a fallback model if the chosen model fails
• Don’t over-route—too many branches can make the system hard to maintain

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If you want to use prompts themselves to route

You can ask a small model:

“Classify this user request into one of: code, summary, creative, support, other. Return only the label.”

Then use that label to pick a model.

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If you want, I can show you:

1. a Python example using OpenAI API,
2. a Node/TypeScript example, or
3. a routing design for production apps.