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In this video, I walk you through n8n's evaluation system and show you how to properly test your AI workflows before pushing them into production. Most people skip this critical testing phase, but evaluations help you measure accuracy, token usage, execution time, and overall workflow performance before a business relies on it.
I cover all five evaluation types built into n8n: categorization, correctness, tools used, string similarity, and helpfulness. You'll learn when to use each type, how to set up test datasets in Google Sheets or data tables, and how to track your results over time. I also explain best practices like only changing one variable at a time, documenting your prompts and model settings, and building proper training datasets with enough examples to confidently validate your workflow.
Throughout the video, I show real examples using AI agents, information extractors, and summarization chains. You'll see how to configure the evaluation trigger, set metrics nodes, and use the check evaluation node to branch your workflow logic. By the end, you'll know exactly how to test your AI workflows systematically, improve accuracy through iteration, and avoid pushing untested automations into production that could fail unexpectedly.
TIMESTAMPS
00:00 Introduction to AI Workflow Evaluation
01:35 Understanding Evaluations in n8n
03:02 Where to Find Evaluation Results
04:07 Creating Your Training Dataset
05:17 What to Change When Testing
07:32 Setting Up Evaluation Nodes
09:53 Example 1: Categorization (Correctness)
13:40 Running Your First Evaluation
16:30 Example 2: Tools Used Evaluation
20:00 Example 3: String Similarity
23:00 Example 4: Helpfulness Metric
25:00 Example 5: Correctness with Data Tables
27:00 Final Thoughts and Best Practices
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