Route AI queries cost‑efficiently with GPT‑4o‑mini, GPT‑4o and confidence scoring

This workflow implements a cost-optimized AI routing system using n8n. It intelligently decides whether a request should be handled by a low-cost model or escalated to a higher-quality model based on response confidence.

The goal is to minimize LLM usage costs while maintaining high answer quality.

A query is first processed by a cheaper model. The response is then evaluated by a confidence-scoring AI agent. If the response quality is insufficient, the workflow automatically escalates the request to a more capable model.

This approach is useful for building scalable AI systems where most queries can be answered cheaply, while complex queries still receive high-quality responses.

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How It Works

1. Webhook Trigger - Receives a user query from an external application.

2. Workflow Configuration - Defines parameters such as: - confidence threshold - cheap model cost - expensive model cost