Evaluate RAG response accuracy with OpenAI: document groundedness metric
Ensure your AI agents remain truthful by automatically measuring how well their responses align with provided source documents. This workflow utilizes OpenAI to generate a groundedness score, effectively identifying potential hallucinations in your RAG pipelines. It's an essential auditing tool for developers looking to optimize retrieval accuracy and maintain high data integrity.
Run this with your team's AIWhat This Recipe Does
Deploying AI at scale requires more than just a powerful model; it requires rigorous oversight to ensure every output aligns with your corporate safety and compliance standards. This automation provides a robust framework for evaluating AI performance and maintaining strict quality control. By integrating automated evaluation triggers, the system systematically reviews model outputs for potential risks, hallucinations, or policy violations. This eliminates the need for manual spot-checks and provides a documented audit trail of AI behavior. Businesses can confidently accelerate their AI adoption, knowing that a consistent layer of governance is monitoring every interaction. This workflow transforms high-level safety guidelines into an actionable, automated process that protects your brand reputation and minimizes operational risk. Whether you are deploying customer-facing chatbots or internal knowledge assistants, this solution ensures that your AI remains a safe and reliable asset. It moves your team from reactive troubleshooting to proactive compliance management, allowing you to focus on innovation while the system handles the complexities of safety monitoring.
What your team gets
Forms and dashboards, so it is not a script only one person understands
Runs on your schedule in the cloud, so it does not stop when a laptop closes
Endpoints, so the rest of your stack can trigger the same work
ManualTrigger, HttpRequest, Langchain.vectorStoreInMemory, Langchain.embeddingsOpenAi, Langchain.documentDefaultDataLoader connected for the team, not per person
How It Works
- 1
Open the recipe and connect your accounts
Connect ManualTrigger and HttpRequest once, in your team cloud, and nobody has to do it again on their own machine
- 2
Tell your own agent what is different about your process
Claude, ChatGPT, Cursor, whichever your team already uses. It adapts the recipe to how you actually work
- 3
Run it, then leave it running
It lives in your team cloud, so it keeps going after you close the laptop and every teammate's AI can use it
Who Uses This
- Compliance officers use this to automatically flag and review AI responses that may violate industry regulations or internal data privacy policies.
- Customer experience managers implement this to ensure AI-generated support responses maintain a professional tone and provide accurate information.
- Product teams use this during testing to benchmark different AI models against safety datasets, ensuring the most reliable version is deployed to production.
Frequently Asked Questions
Can I define my own safety criteria?
Yes, the system is fully customizable, allowing you to set specific rules and evaluation parameters that align with your unique business requirements and industry standards.
Does this work with any AI model?
The automation uses standard HTTP requests and evaluation nodes, making it compatible with most major LLM providers and proprietary internal models.
How does this improve our current compliance process?
It replaces manual, inconsistent reviews with a standardized, automated evaluation process that operates at scale and provides immediate feedback on output quality.
What kind of data does this capture?
The workflow captures detailed evaluation results that can be used to generate compliance logs, performance trends, and risk assessment summaries for stakeholders.
Coming from n8n?
This recipe uses nodes like ManualTrigger, HttpRequest, Langchain.vectorStoreInMemory, Langchain.embeddingsOpenAi and 12 more. On Runwork, you don't need to learn n8n's workflow syntax. Describe what you want to your own AI agent in plain English.
Based on n8n community workflow. View original
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Run this with the AI your team already uses
Your agent adapts it, your team cloud keeps it running, and everyone's AI can find it.
Open this recipe in Runwork