Overview

Practical strategies for getting AI coding agents to generate high-quality Python tests, focusing on leveraging existing code patterns and pytest ecosystem knowledge. The key insight is that agents learn best from good examples rather than detailed instructions.

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The Breakdown

  • Python’s rich pytest ecosystem in training data means agents understand advanced testing patterns like mocking and fixtures without explicit instruction
  • The most effective approach is working within projects that already have clean test suites - agents naturally imitate existing patterns
  • Common anti-pattern to watch for is duplicated test setup code, which can be fixed by prompting for pytest.mark.parametrize or fixture extraction
  • Cloning existing well-tested projects as reference examples is more effective than writing detailed testing requirements from scratch