Stop guessing whether your AI classifier is working—start measuring its performance with precision. This n8n workflow template provides a complete evaluation system for AI classifiers, letting you test how well your support ticket classifier performs against real data with exact match scoring. What this workflow does The template includes a production path where webhooks receive support tickets, an AI Agent classifies them by category and urgency, and returns responses. The built-in evaluation system feeds test data from Data Tables through your AI Agent, comparing predictions against expected labels. You'll run tests through the Evaluations tab, inspect per-case scores and aggregate metrics, then tweak prompts or models and compare runs side by side to optimize performance. Key learning outcomes Master n8n's Evaluation Trigger, Data Tables, and Evaluation node integration Use "Check if Evaluating" operations to separate evaluation traffic from production Score structured AI outputs against correct answers using exact match methods Build test sets from real execution history instead of synthetic examples Use cases Support ticket routing: Ensure customer inquiries reach the right departments consistently Content moderation: Validate AI classification accuracy for user-generated content Quality assurance: Catch classifier regressions before they impact users Model optimization: Compare different prompts and models with data-driven insights Technical details This workflow leverages essential n8n nodes including webhook triggers, LangChain AI Agent integration, evaluation nodes, code execution, sticky notes for documentation, and respond to webhook functionality. The template creates a repeatable evaluation path alongside your production classifier workflow. Perfect for automation engineers and SaaS operators who need reliable AI performance monitoring. Classification accuracy can silently degrade when inputs shift—this workflow gives you the tools to measure quality, prevent regressions, and ship prompt changes based on solid data rather than intuition.
Related
Related
Related
Related