Answer to: Improve the RAG chatbot result

You can set a minimum threshold and short-circuit if all retrieved docs are below it, but that should just be your first gate, not the only one. A better pattern in LangChain is to introduce an LLM-based grading step before you generate the final answer. After retrieving documents, send the user query plus the retrieved chunks to a small grading prompt. Ask the model to return structured output like “relevant: true/false” and maybe a confidence score. If the grader says the docs are not relevant enough, you either trigger your fallback (web search tool) or return a controlled “I don’t know” response. You can also add a second validation step after generation. Generate the answer strictly from the provided context, then run another LLM pass that checks: “Is this answer fully supported by the provided documents?” If the validator detects unsupported claims, you reject the answer and either retry with different retrieval or return “I don’t know.” In LangChain or LangGraph this is usually implemented as a conditional branch in your chain rather than a simple linear pipeline.