Faithfulness

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 FTL Faithfulness Model, which evaluates factual consistency between AI-generated responses and their source context in RAG applications.

This tutorial covers FTL Faithfulness (ftl_response_faithfulness) — Fiddler's proprietary Fast Trust 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, get your API key from the sign-up page below. See the documentation and FAQs for more help with getting started.

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Click this link to get started using Google Colab →

Or download the notebook directly from GitHub.