How to Monitor LLM Hallucinations in Real-Time
Learn how to use evaluation hooks and safety thresholds to catch AI errors before they reach users.
You can't stop every hallucination, but you can catch them. Real-time monitoring is the only way to build a trustworthy AI product.
Using Faithfulness Hooks
Integrate an automated evaluation tool (like LangSmith or Phoenix) that triggers on every response. The system runs a quick "fact-check" by comparing the AI's answer against the retrieved context. if the "faithfulness score" falls below a certain threshold (e.g., 0.8), the response is flagged or blocked.
Self-Correction Prompts
You can also use the model itself for monitoring. Before showing the answer to the user, ask the model a follow-up: "Are there any factual contradictions in your previous response?" This simple step often causes the model to identify its own hallucinations, allowing you to provide a more reliable experience.