The latest articles on DEV Community by Olivier Cohen (@olivier_cohen_ragmet). https://dev.to/olivier_cohen_ragmet https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3902967%2F43cab4bd-2f4e-4435-b633-ca4c4754d5a7.jpeg https://dev.to/olivier_cohen_ragmet en Olivier Cohen Tue, 28 Apr 2026 18:40:29 +0000 https://dev.to/olivier_cohen_ragmet/how-to-detect-ai-hallucinations-inside-n8n-ragmetrics-node-walkthrough-2ncd https://dev.to/olivier_cohen_ragmet/how-to-detect-ai-hallucinations-inside-n8n-ragmetrics-node-walkthrough-2ncd <p>If you're running LLM outputs through n8n workflows, you probably have no systematic way to verify what the model actually produced.<br> Did it hallucinate? Did it stay grounded in your source data? Was the answer accurate?<br> We just launched a native n8n node for RagMetrics that solves this.<br> How it works<br> 3 nodes and you're evaluating every AI output in your workflow:<br> Trigger → Edit Fields → RagMetrics Evaluation<br> The RagMetrics node accepts:</p> <p>question — the original user query<br> answer — the model-generated response<br> ground_truth — the correct expected answer<br> context — source documents for grounding evaluation<br> conversation — session ID for grouping evaluations<br> evaluation_group — your RagMetrics criteria configuration</p> <p>And returns structured JSON with:</p> <p>Criteria name (Accuracy, Hallucination, Grounding etc)<br> Score 1–5<br> Detailed reasoning for the score<br> Token usage for cost tracking</p> <p>What you can do with the score<br> Once you have a score in your workflow you can:<br> → Route outputs below a threshold to a human review queue<br> → Trigger Slack or email alerts when hallucination is detected<br> → Log every evaluation to your RagMetrics dashboard automatically<br> → Block downstream actions when quality is too low<br> Two evaluation methods<br> Live AI Evaluation — uses a pre-configured Evaluation Group for consistent scoring across multiple evaluations. Ideal for production monitoring and batch processing.<br> Direct Evaluation API — submit single question-answer pairs for immediate scoring without an Evaluation Group. Perfect for ad-hoc evaluations and quick testing.<br> Quick setup</p> <p>Create a RagMetrics account at ragmetrics.ai<br> Configure your judge model API key in the dashboard<br> Create an Evaluation Group and select your criteria<br> Add your RagMetrics API key to n8n credentials<br> Add the RagMetrics Evaluation node to your workflow<br> Map your fields and connect to downstream logic</p> <p>Get started<br> 📄 Node documentation: ragmetrics.ai/resources/n8n-node<br> ⚡ Starter workflow ready to import: ragmetrics.ai/resources/n8n-simple-workflow<br> Questions or want help getting set up:<br> 📧 <a href="mailto:olivier@ragmetrics.ai">olivier@ragmetrics.ai</a><br> 📱 +1 917 767 4075</p> n8nbrightdatachallenge ai rag llm