Detect AI-Generated Hallucinations with Custom Screening

How it works

1. 1.Initiate workflow manually or via external trigger to begin hallucination screening
2. 2.Parse and segment input text into analyzable chunks using custom code logic
3. 3.Execute parallel LLM chains (Ollama and LLM Chat) to identify factual inaccuracies and false claims
4. 4.Filter and rank detected hallucinations by severity level and confidence score
5. 5.Merge and structure final results for reporting, logging, or automated response

Use cases

• Verify AI responses before publication to minimize misinformation spread
• Audit social media content from Twitter/X to fact-check shared information
• Validate local LLM model outputs and iteratively improve prompt quality based on detected errors

Requirements

• Deployed and properly configured Ollama model (local or cloud-hosted)
• Twitter/X API credentials if ingesting content from the social platform
• Structured or semi-structured text input with clear source attribution
• Database or logging system to track screening history and audit trails

Service Value

Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.