This pattern adds a governance gate before retrieval. Instead of always running RAG, a classifier node first labels the question as on-topic/off-topic for a constrained domain (for example one company knowledge base).
State design in the source note flow:
messages for conversation content,documents for retrieved chunks,onTopic (yes/no) for routing decisions.
Classifier implementation details: use a structured output schema (Pydantic model) that forces a strict label rather than free-form prose. This creates reliable routing behavior.
Routing contract:
- On-topic: retrieve relevant docs -> generate answer from retrieved context.
- Off-topic: skip retrieval + generation, return fixed safe response.
Why teams use this in production: lower hallucination risk, tighter domain boundaries, and lower token/API cost by avoiding unnecessary retrieval/generation for irrelevant prompts.
Important tradeoff: you gain policy control but classifier quality now directly impacts user experience. False negatives can reject valid questions.
Deepening Notes
Source-backed reinforcement: these points are extracted from the LangGraph source note to sharpen architecture and flow intuition.
- And then coming down we have the questions classifier which is going to be this first node right here.
- on topic router so we're taking this particular we're getting reference to this thing and then we're checking is it yes or no.
- right after the topic decision, we are going to the router which is going to decide if it's going to go to the on topic route or the off topic route.
- in the final state object you can see that we have the messages we have the documents and we have the on topic.
- In the next section we are going to take a look at you know how we can actually provide a rag node as a tool to the agent so that the agent can you know invoke the tool anytime it wants to.
Interview-Ready Deepening
Source-backed reinforcement: these points add detail beyond short-duration UI hints and emphasize production tradeoffs.
- Route on-topic questions through retrieval and block off-topic requests with deterministic graph control.
- Off-topic: skip retrieval + generation, return fixed safe response.
- But if it is not we are going to the off topic route.
- Instead of always running RAG, a classifier node first labels the question as on-topic/off-topic for a constrained domain (for example one company knowledge base).
- Why teams use this in production: lower hallucination risk, tighter domain boundaries, and lower token/API cost by avoiding unnecessary retrieval/generation for irrelevant prompts.
- Aggressive grounding reduces hallucinations but can increase abstentions when retrieval coverage is weak.
- This pattern adds a governance gate before retrieval.
- Classifier implementation details: use a structured output schema (Pydantic model) that forces a strict label rather than free-form prose.
Tradeoffs You Should Be Able to Explain
- Higher recall often increases context noise; reranking and filtering are required to keep precision high.
- Smaller chunks improve semantic precision but can break cross-sentence context needed for accurate answers.
- Aggressive grounding reduces hallucinations but can increase abstentions when retrieval coverage is weak.
First-time learner note: Think in state transitions, not giant prompts. Keep node responsibilities small and route logic deterministic so each step is easy to reason about.
Production note: Bound autonomy with loop limits, tool policies, and checkpoints. Capture route decisions and state snapshots for replay and incident analysis.