Automated LLM Evaluation for Legal AI Quality Assurance
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Elevate the reliability of your legal Large Language Models (LLMs) with this n8n workflow. It automates the rigorous evaluation process, comparing AI-generated outputs against source materials to ensure factual correctness, relevance, and completeness in the demanding legal domain.
About This Workflow
This n8n workflow offers a robust, automated solution for systematically assessing LLM performance in specialized legal contexts. Utilizing a sophisticated LangChain LLM evaluator, it meticulously checks AI assistant outputs against provided source documents and tasks. The integrated prompt defines strict accuracy standards, including factual correctness, relevance to the query, and completeness, while also highlighting common failure patterns specific to legal information processing. Designed for legal tech developers and QA teams, this automation streamlines your quality assurance pipeline by pulling test cases from Google Sheets, executing detailed AI evaluations, and structuring the results for insightful analysis, ensuring your LLM applications meet the highest quality benchmarks.
Key Features
- Automated LLM Evaluation: Executes AI-powered evaluations on LLM responses using a meticulously crafted, domain-specific prompt for consistency.
- Legal Domain Focus: Incorporates explicit accuracy standards and identifies common failure patterns tailored for legal information extraction and response generation.
- Structured Output & Parsing: Generates a clean JSON evaluation output (including reasoning and a clear Pass/Fail decision) that is automatically parsed for easy integration and analysis.
- Google Sheets Integration: Seamlessly retrieves test cases, source documents, and AI-generated outputs directly from Google Sheets for scalable batch evaluation.
- Conditional Processing: Includes logic to differentiate and potentially handle various input types, such as PDF documents, for adaptable evaluation strategies.
How To Use
- Configure Google Sheets (Get Tests): Update the 'Document ID' and 'Sheet Name' in the 'Get Tests' node to link to your Google Sheet. Ensure your sheet contains columns for the LLM task (e.g., 'Task' or 'Input'), the source material (e.g., 'Source Text'), and the AI Assistant's output to be evaluated (e.g., 'AI Output'). Confirm your Google Sheets credentials are set up.
- Customize LLM Prompt (Basic LLM Chain1): Access the 'Basic LLM Chain1' node and review the extensive evaluator prompt. Modify it as needed to align with your specific evaluation criteria or nuances within your legal domain.
- Adjust Output Schema (Structured Output Parser2): In the 'Structured Output Parser2' node, verify that the 'JSON Schema Example' accurately reflects the expected JSON output format from your LLM's evaluation (e.g.,
{"reasoning": "...", "decision": "..."}). - Connect Evaluation Results (Merge1): Ensure the output of the 'Structured Output Parser2' is correctly linked to the 'Merge1' node. This will combine the evaluation results with the original test case data for a comprehensive view.
- Trigger and Monitor: Initiate evaluations manually using the 'When clicking ‘Test workflow’' node for ad-hoc testing, or integrate the 'Webhook' node as an API endpoint if you wish to programmatically submit evaluation requests from other systems.
Apps Used
Workflow JSON
{
"id": "e01e6d76-1528-4a3d-b516-ba8b8719150e",
"name": "Automated LLM Evaluation for Legal AI Quality Assurance",
"nodes": 26,
"category": "Operations",
"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: e01e6d76-1528...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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