Evaluate RAG Response Accuracy with OpenAI: Document Groundedness Metric

Introduction

This n8n template demonstrates how to calculate the evaluation metric “RAG document groundedness,” which measures an AI agent’s ability to generate responses strictly based on information retrieved from vector store documents. Adapted from Google’s Vertex AI scoring approach, this template integrates OpenAI and Google Sheets to quantify how well an LLM adheres to grounded data, identifying potential hallucinations or inaccuracies in AI responses.

Key Benefits

• Automates assessment of AI response accuracy against retrieved documents
• Helps detect hallucinated or unsupported information in AI-generated outputs
• Integrates easily with vector store-based retrieval systems
• Provides clear scoring to improve prompt or model designs
• Utilizes n8n’s low-code automation workflow for seamless implementation

Ideal For

• AI Engineers
• Data Scientists
• Machine Learning Researchers
• Prompt Engineers
• Product Managers in AI-focused teams

Relevant Industries

• Artificial Intelligence
• Software Development
• Research & Development
• Data Analytics
• Technology Consulting

Included Products

• OpenAI (AI & Machine Learning)
• Google Sheets (Spreadsheet)

Alternative Products

• AI & Machine Learning: Cohere, Anthropic
• Spreadsheet: Microsoft Excel, Airtable

Expansion Options

• Integrate additional vector databases like Pinecone or Weaviate
• Extend scoring with granular error type classification
• Automate corrective prompt generation based on low groundedness scores
• Visualize scoring trends via dashboards such as Grafana or Google Data Studio
• Incorporate multi-language support for global datasets