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Learn how to implement the Centor Model for Faithfulness, which evaluates factual consistency between AI-generated responses and their source context in RAG applications.
This tutorial covers the Centor Model for Faithfulness (ftl_response_faithfulness) — Fiddler’s proprietary Centor Model for real-time guardrail use cases. For the LLM-as-a-Judge RAG Faithfulness evaluator used in Agentic Monitoring and Experiments, see the RAG Health Metrics Tutorial.
Inside you’ll find:
  • Step-by-step implementation instructions
  • Code examples for evaluating response accuracy
  • Practical demonstration of hallucination detection
  • Sample inputs and outputs with score interpretation
Before running the notebook, generate a Fiddler API key from Settings → Credentials in your Fiddler environment. See the Guardrails documentation and FAQ for more help getting started.
Interactive TutorialRun the Faithfulness Guardrail notebook end to end:Open the Faithfulness Guardrail Notebook in Google Colab →Or download the notebook from GitHub →