Comprehensive guide covering Testing AI-Generated Code: Strategies That Actually Work with practical implementation details.

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A practical workflow for testing AI-generated code covers four strategies: writing failing tests before prompting the AI (test-first prompting with Jest), property-based testing with fast-check to surface unanticipated edge cases, end-to-end validation with Playwright for UI and integration points, and adversarial AI review prompts to catch hallucinated APIs. The guide includes concrete code examples showing common AI failure patterns (hallucinated methods, off-by-one errors, happy-path-only logic, stale API usage), corrected implementations, and a CI integration checklist. Key rule: never let AI both generate and validate its own code.

Testing AI-Generated Code: Practical Strategies

The Testing Dilemma: Code You Didn't Write (and Don't Fully Understand)

Why Standard Testing Isn't Enough for AI Code

Strategy 1: Write Tests Before You Prompt

Strategy 2: Property-Based Testing to Catch What You Didn't Think Of

Strategy 3: End-to-End Validation with Playwright

Strategy 4: Use AI to Test AI (Adversarial Review Prompts)

Putting It All Together: A Realistic Workflow