Evaluation metric example: String similarity

AI evaluation in n8n This is a template for n8n's evaluation feature. Evaluation is a technique for getting confidence that your AI workflow performs reliably, by running a test dataset containing different inputs through the workflow. By calculating a metric (score) for each input, you can see where the workflow is performing well and where it isn't. How it works This template shows how to calculate a workflow evaluation metric: text similarity, measured character-by-character. The workflow takes images of hand-written codes, extracts the code and compares it with the expected answer from the dataset. The images look like this: The workflow works as follows: - We use an evaluation trigger to read in our dataset - It is wired up in parallel with the regular trigger so that the workflow can be started from either one. More info - We download the image and use AI to extract the code - If we’re evaluating (i.e. the execution started from the evaluation trigger), we calculate the string distance metric - We pass this information back to n8n as a metric