RAG Response Quality Check with OpenAI Groundedness Metric

Say goodbye to unreliable AI agent outputs!

This automation evaluates whether your AI agent's responses are grounded in the documents retrieved from a vector store — helping you detect hallucinations and improve prompt quality. It’s a must-have for anyone building RAG (retrieval-augmented generation) workflows.

Whether you're testing agents, optimizing prompt chains, or improving system reliability, this tool ensures your AI stays aligned with its sources.

What it does:

📥 Collects both the agent's response and the retrieved documents
🧠 Uses an LLM to evaluate if the response is grounded in the source material
🚫 Flags hallucinated content not found in the documents
📊 Scores each response for adherence and alignment
📁 Logs results into Google Sheets for easy review and iteration

✅ Setup guide & sample evaluation sheet included

Need help setting it up? We offer end-to-end integration, evaluation tuning, and optimization support for document-based agents.