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This Quick Start notebook introduces Fiddler Guardrails, an enterprise solution that safeguards LLM applications from risks like hallucinations, toxicity, and jailbreaking attempts. 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 →