> ## 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.

# LLM Monitoring

> Monitor LLM applications in production with Fiddler. Track quality, safety, and performance enrichments, detect problematic responses, and diagnose issues before they reach users.

## Fiddler LLM Monitoring Introduction

### What Is LLM Monitoring?

Large Language Models (LLMs) are powerful, but they introduce unique challenges around accuracy, safety, and reliability. Effective monitoring is critical for detecting issues like hallucinations, toxic content, and performance degradation in production LLM applications.

### How Fiddler LLM Monitoring Works

Fiddler's LLM monitoring solution tracks your AI application's inputs and outputs, then enriches this data with specialized metrics that measure quality, safety, and performance. These enrichments provide visibility into how your LLM applications behave in production, enabling you to:

* Detect problematic responses before they impact users
* Identify patterns of failure across your applications
* Track performance trends over time
* Analyze root causes when issues occur

### Key Capabilities

* **Comprehensive Metrics**: Monitor hallucinations, toxicity, relevance, latency, and many other LLM-specific metrics
* **Real-time Analysis**: Track performance as it happens with intuitive dashboards
* **Advanced Enrichments**: Generate embeddings, similarity scores, and specialized trust metrics automatically
* **Drift Detection**: Identify when prompts or responses drift from expected patterns
* **RAG-specific Monitoring**: For retrieval-augmented applications, analyze retrieval quality and source relevance

### Getting Started

Implementing Fiddler LLM monitoring requires just three steps:

<Steps>
  <Step title="Onboard your LLM application">
    Define its inputs, outputs, and which enrichment metrics you need.
  </Step>

  <Step title="Publish your application data">
    Send prompts, responses, and context to Fiddler.
  </Step>

  <Step title="Monitor performance">
    Use dashboards and alerts to track the metrics most important to your use case.
  </Step>
</Steps>

Fiddler handles the complex work of generating enrichments, detecting anomalies, and providing the visualizations you need to maintain high-quality LLM applications.

### Next Steps

<Columns cols={3}>
  <Card title="Quick Start" icon="rocket" href="/developers/quick-starts/simple-llm-monitoring">
    Stand up LLM monitoring end to end in about 10 minutes.
  </Card>

  <Card title="Learn" icon="book-open">
    * [Understanding LLM enrichment metrics](/observability/llm/enrichments)
    * [How Fiddler generates LLM metrics](/observability/llm/llm-based-metrics)
    * [Available LLM metrics](/observability/llm/selecting-enrichments)
    * [Create LLM visualizations using embeddings](/observability/llm/embedding-visualization-with-umap)
  </Card>

  <Card title="Reference" icon="code">
    * [Fiddler Python client SDK](/sdk-api/python-client/connection)
    * [Fiddler Python client SDK guides](/developers/python-client-guides/installation-and-setup)
  </Card>
</Columns>