Overview
This tutorial teaches developers how to guide AI coding agents to write high-quality Python tests. The core insight is that agents learn from existing patterns - providing good test examples in your codebase leads to better AI-generated tests without additional prompting.
What You’ll Learn
- use existing tests as templates - show agents examples from well-tested projects to guide their output
- leverage pytest’s rich ecosystem - agents understand commands like “use pytest-httpx to mock endpoints” due to extensive training data
- refactor with specific pytest commands - use “pytest.mark.parametrize” and “extract common setup into fixtures” to improve duplicated code
- maintain clean baseline tests - agents naturally match the quality patterns already present in your test suite
- clone reference repositories - point agents to specific GitHub projects that demonstrate your preferred testing patterns
Prerequisites
Basic Python and pytest knowledge, familiarity with AI coding agents like Claude