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v0.23.x
v0.23.x
  • Get Started
  • Overview
  • Getting Access to Distributional
  • Install the Python SDK
  • Quickstart
  • Learning about Distributional
    • Distributional Concepts
    • Why We Test Data Distributions
    • The Flow of Data
  • Using Distributional
    • Projects
    • Runs
      • Reporting Runs
      • Setting a Baseline Run
    • Metrics
    • Tests
      • Creating Tests
        • Using Filters in Tests
        • Available Statistics and Assertions
      • Running Tests
      • Reviewing Tests
        • What Is a Similarity Index?
    • Notifications
    • Access Controls
      • Organization and Namespaces
      • Users and Permissions
      • Tokens
  • Platform
    • Sandbox
    • Self-hosted
      • Architecture
      • Deployment
        • Helm Chart
        • Terraform Module
      • Networking
      • OIDC Authentication
      • Data Security
  • Reference
    • Query Language
      • Functions
    • Python SDK
      • dbnl
      • dbnl.util
      • dbnl.experimental
      • Classes
      • Eval Module
        • Quick Start
        • dbnl.eval
        • dbnl.eval.metrics
        • Application Metric Sets
        • How-To / FAQ
        • LLM-as-judge and Embedding Metrics
        • RAG / Question Answer Example
      • Classes
  • CLI
  • Versions
    • Release Notes
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Testing Strategies

Like in traditional software testing, it is paramount to come up with a testing strategy that has both breadth and depth. Such a set of tests gives confidence that the AI-powered app is behaving as expected, or it alerts you that the opposite may be true.

To build out a comprehensive testing strategy it is important to come up with a series of assertions and statistics on which to create tests. This section explains several goals when testing and how to create tests to assert desired behavior.

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