> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fiddler.ai/llms.txt
> Use this file to discover all available pages before exploring further.

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

<Info>
  **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](/developers/tutorials/experiments/rag-health-metrics-tutorial).
</Info>

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](/reference/administration/settings#credentials)** in your Fiddler environment. See the [Guardrails documentation](/protection/guardrails) and [FAQ](/protection/guardrails-faq) for more help getting started.

<Info>
  **Interactive Tutorial**

  Run the Faithfulness Guardrail notebook end to end:

  [**Open the Faithfulness Guardrail Notebook in Google Colab →**](https://colab.research.google.com/github/fiddler-labs/fiddler-examples/blob/main/quickstart/latest/Fiddler_Quickstart_Guardrails_Faithfulness.ipynb)

  [**Or download the notebook from GitHub →**](https://github.com/fiddler-labs/fiddler-examples/blob/main/quickstart/latest/Fiddler_Quickstart_Guardrails_Faithfulness.ipynb)
</Info>