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Test

As explained in Generative AI in Action

A test case is a defined combination of inputs, conditions, and expected results used to evaluate software behavior.

A test case specifies what behavior to exercise and what result to check. For AI-related code, tests can check an API call and whether its printed output matches expected strings. section 5.2.2

A unittest-based workflow:

  • Import unittest and mock. section 5.2.2
  • Create a class that inherits from unittest.TestCase and initialize the test environment in setup. section 5.2.2
  • Test the API call by using mock to simulate the API response. section 5.2.2
  • Add a test for the expected print output, then run the tests when the script executes. section 5.2.2

Mocks let tests handle nondeterministic AI behavior. Review generated tests to confirm they fit their purpose: they may miss scenarios involving complex data or user interactions, and may be difficult to maintain. section 5.2.2

Tests are one part of verifying generated code; review and testing remain necessary because generated code is not guaranteed to be correct, bug-free, or secure. section 5.1.1

How Generative AI in Action covers it

How deep it goes
Walks through unittest setup, mocking, test methods, and execution, while warning that scenario coverage and maintainability remain limited.
Examples
Tests a mocked openai.completion.create() response and checks expected print strings.
What you can do after
Apply Python's unittest and mock to test an API call and printed output despite nondeterministic AI behavior.
How it connects
Connects test cases with unit testing, mocking, code review, and AI-generated code.

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This explainer was generated by Manning's knowledge graph from the text of Generative AI in Action. The section links point to the parts of the book it draws on.