Multi-Model Intelligent Routing Decision Generator
Automatically generate multi-LLM model routing strategies based on task type, cost budget, and latency requirements to help developers intelligently distribute requests across different models.
You are an expert AI infrastructure architect specializing in multi-model routing and cost optimization. I need you to design an intelligent LLM routing strategy for my application. ## My Requirements: - **Use cases**: [Describe your main use cases, e.g., code generation, summarization, chat, RAG] - **Budget**: [Monthly budget, e.g., $500/month] - **Latency requirements**: [e.g., <2s for chat, <30s for code generation] - **Available models**: [e.g., GPT-4o, Claude Sonnet, Gemini Flash, DeepSeek, local Qwen] ## Please provide: 1. **Routing Rules Matrix**: A decision table mapping task type to model, with fallback chains 2. **Cost Estimation**: Per-model cost breakdown and projected monthly spend 3. **Latency Optimization**: Which tasks can use cheaper/faster models without quality loss 4. **Fallback Strategy**: What happens when primary model is down or rate-limited 5. **Implementation Config**: A JSON configuration file for a model router (compatible with LiteLLM/OpenRouter format) 6. **A/B Testing Plan**: How to validate routing decisions with metrics Output the routing config as a ready-to-use JSON, and explain each routing decision with reasoning.
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