Automated LLM Testing and Tracking with GPT-4 Judge & Google Sheets

Overview

This workflow streamlines the process of testing large language models (LLMs) by automating test execution, evaluation, and result tracking. It leverages GPT-4 as an automated judge and integrates with Google Sheets for centralized result management.

Key Features

• Executes LLM test cases automatically via a webhook trigger.
• Uses GPT-4 (via OpenRouter) to evaluate and judge test outputs.
• Parses structured outputs for consistent result formatting.
• Merges and limits data for efficient processing.
• Updates Google Sheets in real-time to track test results and analytics.

Benefits

• Reduces manual effort in LLM testing and evaluation.
• Ensures consistent, unbiased scoring using AI-powered judgment.
• Centralizes test results for easy access and reporting.
• Saves time by automating repetitive data processing and tracking tasks.

Use Cases

• AI/ML teams validating LLM performance across multiple scenarios.
• QA teams automating regression testing for conversational AI products.
• Data scientists tracking model improvements over time.

Integrations & Automation

Key integrations include Google Sheets for data tracking and OpenRouter for GPT-4 access. The workflow uses n8n's automation capabilities to orchestrate data extraction, evaluation, and storage, delivering significant efficiency gains.