Waterflai
  • Welcome to Waterflai
  • Getting Started
    • Concepts
    • Quickstart
  • Providers
    • Providers Overview
    • Providers setup
    • AI models
    • Choose the right models
  • Knowledge
    • Knowledge Overview
    • Knowledge connectors
    • Knowledge collections
  • Studio
    • Studio Overview
    • Studio Builders
      • Light Builder
      • Dream Builder
      • Workflow Builder
      • Flow components (nodes)
        • Input Node
        • Output Node
        • LLM model Node
        • Multimodal LLM Node
        • Dall-E 2 (image generation) Node
        • Dall-E 3 (image generation) Node
        • Sora video generation Node
        • Text-to-Speech (TTS) Node
        • Speech-to-Text (STT) Node
        • OCR Node
        • Agent Node
        • Reranker Node
        • Knowledge retrieval Node
        • Vector store insert Node
        • Vector store record delete Node
        • Gitbook loader
        • Notion Database Node
        • Figma Node
        • Webpage scraper Node
        • Sitemap Scraper Node
        • API Request Node
        • Document metadata extraction Node
        • Document metadata update Node
        • Character splitter Node
        • HTML splitter Node
        • Markdown Splitter
        • Calculator tool Node
        • Text as tool Node
        • Knowledge retrieval tool Node
        • Conditional Node
        • Iteration loop Node
      • Testing and Debugging
    • Publishing
    • Integration with API
    • Embedding in website
  • Analytics
    • Analytics Overview
    • Dashboards
    • Logs
  • Administration
    • Organization users
    • Workspace
    • Security and permissions
  • Troubleshooting
    • Support
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On this page
  • Accessing the Dashboards
  • Best Practices
  1. Analytics

Dashboards

The Analytics Dashboards in Waterflai provide a comprehensive view of your AI applications' performance and usage. This section will guide you through using and interpreting the various dashboards available.

Accessing the Dashboards

  1. Navigate to the Analytics section in your Waterflai workspace.

  2. You'll see a tabbed interface with different dashboard views: Overview, User Engagement, Performance, and Logs.

Best Practices

  1. Regularly review all dashboard views to get a comprehensive understanding of your AI applications' performance.

  2. Use the date range selector to compare performance across different time periods.

  3. Pay attention to sudden changes in metrics, as they may indicate issues that need addressing.

  4. Use the insights gained from these dashboards to guide optimization efforts and resource allocation.

By effectively using these dashboards, you can ensure your AI applications are performing optimally and meeting user needs.

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Last updated 4 months ago