XALON Tools™
RAG Response Quality Check with OpenAI Groundedness Metric
RAG Response Quality Check with OpenAI Groundedness Metric
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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.
