How does prompt routing architecture decide between retrieval, tools, and pure LLM generation?

Prompt routing classifies incoming queries to decide whether to use retrieval, tools, or direct model generation.

Modern ChatGPT systems use a routing layer before inference to decide how a query should be handled. The router classifies intent, complexity, and external knowledge requirements.

Simple queries are handled directly by the LLM. Knowledge-intensive queries trigger RAG pipelines. Action-based queries invoke tools or APIs.

This architecture improves efficiency, reduces hallucinations, and ensures correct tool usage while minimizing unnecessary computation.