Adaptive RAG redefines traditional RAG by routing queries to vector stores, web search, or direct LLM responses based on intent, effectively handling simple, ambiguous, and out-of-domain questions.

The article offers practical implementation details, including routing, classification, and API exposure code, along with real-world performance notes and links to additional resources.

Intent classification is achieved with a structured router that uses a strict JSON schema via Pydantic to ensure deterministic routing decisions.

Step 2 explains how function-calling or structured output from LLMs classifies questions into destinations like vectorstore, web_search, or direct_llm, with examples and test results.

Step 3 shows integrating the router into an asynchronous FastAPI endpoint, detailing how to route, execute the chosen path, and return results for each destination.

Key production benefits include lowering vector DB costs by filtering trivial questions, employing a zero-hallucination grader to keep context relevant, and reducing latency by skipping unnecessary embeddings and Retrieval steps for direct answers.

The approach defines fallback strategies for ambiguous or out-of-domain queries, such as triggering web search or asking the user for clarification when relevance is uncertain.