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

# Discoverability

> Making your content discoverable and accessible to AI systems

## Overview

Discoverability in Raily refers to how AI systems can find, understand, and access your content. Raily helps you measure and optimize how LLMs discover and select your data and AI Endpoints.

## Why Discoverability Matters

AI systems need to discover the right content at the right time. Effective discoverability ensures:

* **Relevance** - AI finds the most appropriate content for each query
* **Efficiency** - Reduced time and cost in content retrieval
* **Context** - AI understands the purpose and structure of your content
* **Selection** - Your content is chosen by LLMs when relevant

## Discoverability Metrics

Track how your data and AI Endpoints are being selected by LLMs through the Raily dashboard at [app.raily.ai/dashboard/discoverability](https://app.raily.ai/dashboard/discoverability).

### Total Tests

The total number of discoverability tests performed to measure how LLMs find and select your content.

### Discovery Rate

The rate at which your data and AI Endpoints are successfully discovered by LLMs.

### Passed Tests

The number of tests where your data/AI Endpoints were successfully discovered and selected by LLMs.

### Failed Tests

The number of tests where your data/AI Endpoints were not discovered or selected by LLMs.

### Discoverability by Prompt Complexity

A breakdown showing how discoverability varies across different levels of prompt complexity, helping you understand which types of queries surface your content.

### Confusion Matrix

A confusion matrix visualization showing the accuracy of discovery predictions versus actual selections by LLMs.

## Key Elements of Discoverability

### Metadata

Comprehensive metadata helps AI systems understand your content:

* Titles and descriptions
* Categories and tags
* Authors and publication dates
* Topics and keywords
* Language and format

### Content Organization

Structured organization improves AI comprehension:

* Content hierarchies
* Collections and groupings
* Taxonomies and categories
* Related content relationships

### Access Information

Clear information about content availability:

* Access levels and requirements
* License types and terms
* Usage restrictions and permissions
* Pricing and contact information

## Best Practices

<CardGroup cols={2}>
  <Card title="Rich Metadata" icon="database">
    Provide comprehensive, accurate metadata for all content
  </Card>

  <Card title="Consistent Structure" icon="sitemap">
    Use consistent organization and categorization
  </Card>

  <Card title="Monitor Metrics" icon="chart-line">
    Track discoverability metrics to optimize selection rates
  </Card>

  <Card title="Clear Access Terms" icon="file-contract">
    Make licensing and access terms transparent
  </Card>
</CardGroup>

## Next Steps

<CardGroup cols={2}>
  <Card title="Discoverability Analytics" icon="chart-bar" href="/features/discoverability">
    View detailed discoverability metrics and analytics
  </Card>

  <Card title="AI Endpoints" icon="server" href="/concepts/ai-endpoints">
    Expose your data to AI agents via authenticated endpoints
  </Card>
</CardGroup>