Evaluations Metric: Answer Similarity

Introduction

This n8n template demonstrates how to calculate the evaluation metric “Similarity,” which measures the consistency of an AI agent’s responses. Built on a scoring approach adapted from the open-source RAGAS project, the template uses embedding generation and cosine similarity calculation between the AI’s answer and ground truth to produce a similarity score. This method works best for close-ended or fact-based questions with minimal allowable deviation in answers. High similarity scores indicate alignment with expected results, while lower scores can highlight potential hallucinations or inconsistencies in model outputs.

Key Benefits

• Automates AI model response quality evaluation
• Detects hallucinations or inconsistencies in answers
• Leverages cosine similarity for robust comparison
• Open-source based scoring method with transparent logic
• Easy integration with n8n workflows and data sources

Ideal For

• Data scientists monitoring AI performance
• AI developers validating language model outputs
• Machine learning engineers benchmarking models
• QA teams in AI product development
• Technical leads overseeing AI evaluation metrics

Relevant Industries

• Artificial Intelligence
• Software Development
• Data Analytics
• Financial Services
• Healthcare Technology

Included Products

• n8n (Automation Platform)

Alternative Products

• Automation Platforms: Make, Zapier
• Embedding Providers: OpenAI, Cohere, Hugging Face

Expansion Options

• Extend scoring to support open-ended or generative questions
• Integrate with additional data sources like Google Sheets, databases
• Add alerting or reporting based on similarity thresholds
• Combine with other evaluation metrics for comprehensive AI quality checks
• Visualize similarity trends over time using dashboards