Automated Text Processing and Fact-Checking Workflow

This n8n workflow automates the process of splitting, analyzing, and fact-checking large blocks of text, making it highly useful for content verification and data extraction tasks. Starting with a manual trigger, the workflow reads input text and then employs a custom JavaScript code node to split the text into sentences, carefully considering dates and list items to preserve context. These sentences are then split out for individual processing.

Subsequently, the workflow uses language model nodes (integrating Ollama models) to analyze or fact-check the extracted sentences. One of the language models is tailored for fact-checking: it reviews a list of statements, identifies inaccuracies, and summarizes the errors. Another model is used for general AI-based analysis or content generation.

The processed data is aggregated and potentially integrated with other workflows, allowing for seamless, automated content validation. The workflow can be triggered manually for testing or executed by other workflows, making it flexible for various automation scenarios. Overall, this setup can improve content quality, support research, or monitor environmental reports by leveraging AI-driven natural language processing and fact verification.