Automate LLM Evaluation and Reporting with Smart AI Judging
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Streamline your LLM testing process by automatically fetching test cases, judging AI outputs, and updating results in Google Sheets. This workflow leverages AI to assess responses and provides detailed reasoning for quality control.
About This Workflow
This n8n workflow automates the critical process of evaluating Large Language Model (LLM) performance. It begins by fetching a comprehensive suite of test cases directly from a designated Google Sheet, which includes inputs, expected outputs, and AI platform details. Each LLM output is then intelligently judged by an AI model, determining if it meets the reference answer criteria. This judging process also captures a detailed 'reasoning' for the decision, crucial for identifying areas of improvement. Finally, all original test data, along with the AI's judgment and reasoning, is consolidated and appended to a results Google Sheet, providing a clear and actionable overview of LLM performance for ongoing quality assurance and refinement.
Key Features
- Automated Test Case Retrieval: Seamlessly pulls test cases from your Google Sheets to ensure comprehensive evaluation.
- AI-Powered Output Judging: Utilizes AI to objectively assess LLM responses against reference answers, providing pass/fail decisions.
- Detailed Reasoning Capture: Gathers explanations behind AI judgments, enabling deeper insights into LLM behavior.
- Streamlined Results Reporting: Automatically updates a Google Sheet with all test data, decisions, and reasoning for easy analysis.
- Flexible LLM Integration: Easily adaptable to different LLM providers through platforms like OpenRouter.
How To Use
- Configure Data Source: Ensure your Google Sheet is set up with the specified columns: 'ID', 'Test No.', 'AI Platform', 'Input', 'Output', and 'Reference Answer'. The 'ID' column should be unique for each row.
- Set up Google Sheets Integration: Connect your Google account to n8n and authorize access to your Google Sheets.
- Define Output Schema: In the 'Structured Output Parser' node, provide a JSON schema example that the judging AI should adhere to. The provided example uses 'reasoning' and 'decision' fields.
- Specify LLM for Judging: Configure the node responsible for judging the LLM output (likely an AI node, e.g., using OpenRouter) to use your preferred LLM and point it to the relevant inputs from the previous steps.
- Map Output to Google Sheets: In the 'Update Results' node, configure the column mapping to correctly populate your results Google Sheet with data from the executed workflow, including the parsed decision and reasoning.
- Execute Workflow: Click the 'Execute workflow' button to initiate the automated testing and reporting process.
Apps Used
Workflow JSON
{
"id": "fc46890c-5347-46fc-85b6-deea5f12bb5f",
"name": "Automate LLM Evaluation and Reporting with Smart AI Judging",
"nodes": 10,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: fc46890c-5347...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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