Automate AI Model Evaluation with the LLM Judge Workflow
detail.loadingPreview
Streamline your AI model evaluation process by automatically extracting data from documents, processing it with advanced LLMs, and logging the results. This workflow ensures consistent and efficient testing of your AI models against defined criteria.
About This Workflow
The LLM Judge Workflow is a powerful automation solution designed to rigorously evaluate Large Language Models (LLMs). It begins by triggering manually, then retrieves test data from a Google Sheet. For each test, it downloads relevant documents from Google Drive, extracts pertinent information, and sends it to a powerful OpenRouter Chat Model (specifically 'openai/gpt-4.1'). The LLM's output is then parsed into a structured format, and the results, including a decision and reasoning, are logged back into the Google Sheet. A brief pause is included between steps to manage processing.
Key Features
- Automated Data Retrieval: Seamlessly fetch test cases from Google Sheets.
- Document Processing: Automatically download and extract text from files stored in Google Drive (supports PDF extraction).
- Advanced LLM Integration: Leverage state-of-the-art AI models via OpenRouter for intelligent analysis.
- Structured Output Parsing: Consistently capture LLM outputs into a usable JSON format.
- Automated Result Logging: Automatically update your Google Sheets with detailed test outcomes and reasoning.
How To Use
- Trigger: Initiate the workflow by clicking the 'Test workflow' button.
- Data Fetching: The workflow will read test cases from your specified Google Sheet ('Tests' tab in 'LLM Judge' spreadsheet).
- Document Download: For each test, it will download the associated document from Google Drive using the URL provided in your sheet.
- Information Extraction: Extract relevant content, particularly from PDF files.
- LLM Analysis: Send the extracted information and test input to the 'openai/gpt-4.1' model via OpenRouter for evaluation.
- Result Parsing: The LLM's response will be structured into a 'decision' and 'reasoning' based on a predefined JSON schema.
- Result Update: The original test data along with the LLM's 'Decision' and 'Reasoning' will be appended to your Google Sheet for review.
- Controlled Iteration: The 'Loop Over Items' node is configured with
batchSize: 1to process each test case individually, and aWaitnode introduces a small delay between operations.
Apps Used
Workflow JSON
{
"id": "a801e8e9-49e7-4b12-ae4a-d98efc0f6077",
"name": "Automate AI Model Evaluation with the LLM Judge Workflow",
"nodes": 13,
"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: a801e8e9-49e7...
About the Author
SaaS_Connector
Integration Guru
Connecting CRM, Notion, and Slack to automate your life.
Statistics
Related Workflows
Discover more workflows you might like
Effortless Bug Reporting: Slack Slash Command to Linear Issue
Streamline your bug reporting process by instantly creating Linear issues directly from Slack using a simple slash command. This workflow enhances team collaboration by providing immediate feedback and a structured approach to logging defects, saving valuable time for development and QA teams.
Build a Custom OpenAI-Compatible LLM Proxy with n8n
This workflow transforms n8n into a powerful OpenAI-compatible API proxy, allowing you to centralize and customize how your applications interact with various Large Language Models. It enables a unified interface for diverse AI capabilities, including multimodal input handling and dynamic model routing.
Automate Qualys Report Generation and Retrieval
Streamline your Qualys security reporting by automating the generation and retrieval of reports. This workflow ensures timely access to crucial security data without manual intervention.