Automate AI-Powered Text Evaluation with n8n
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This n8n workflow automates the process of evaluating text correctness against a ground truth using advanced AI models. It leverages LangChain's LLM capabilities to classify statements as True Positives, False Positives, or False Negatives, providing detailed reasoning.
About This Workflow
This n8n workflow is designed to streamline the evaluation of AI-generated or user-provided text by comparing it against a definitive ground truth. It utilizes the power of LangChain and OpenAI's sophisticated language models to perform a detailed correctness classification. The process begins by fetching data from a Google Sheet, which contains the question, the generated answer, and the ground truth statements. An AI agent then analyzes these inputs, categorizing each statement in the answer as a True Positive (supported by ground truth), False Positive (not supported), or False Negative (missing from the answer but present in ground truth). Each classification is accompanied by a clear reason, enabling accurate and efficient text quality assessment.
Key Features
- AI-Powered Classification: Accurately categorizes text statements into True Positives, False Positives, and False Negatives.
- Detailed Reasoning: Provides explanations for each classification, enhancing understanding and trust.
- Automated Data Fetching: Seamlessly integrates with Google Sheets to pull evaluation data.
- Configurable LLM Models: Supports integration with powerful AI models like OpenAI's GPT-4.1-mini.
- Structured Output: Generates structured JSON output for easy integration into other systems.
How To Use
- Connect Google Sheets: Configure the 'When fetching a dataset row' node with your Google Sheet's document ID and sheet name.
- Define Input Fields: In the 'Set Input Fields' node, map the 'input' and 'ground truth' columns from your spreadsheet to the respective n8n fields.
- Configure AI Analysis: Set up the 'OpenAI Chat Model' nodes and ensure your OpenAI API credentials are connected.
- Define Classification Prompt: In the 'Correctness Classifier' node, clearly define the prompt that instructs the LLM on how to classify statements (TP, FP, FN) and provide reasons.
- Structure Output: Utilize the 'Examples1' node to provide a JSON schema example for structuring the LLM's output, ensuring consistency.
- Enable Evaluation Trigger: The 'Evaluation' node can be configured to determine if the workflow should proceed with evaluation.
Apps Used
Workflow JSON
{
"id": "f54635e2-d6d9-4738-98ec-c21426822d8c",
"name": "Automate AI-Powered Text Evaluation with n8n",
"nodes": 29,
"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: f54635e2-d6d9...
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