Why API Test Generation Is a Judgment Problem, Not a Code Generation Problem

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When we started using large language models for API test generation at KushoAI, the results were impressive on the surface. Tests appeared in seconds. Coverage breadth went up. The team was excited. Then we looked more carefully at what was being produced. On one endpoint, the model generated 26 tests where a senior QA engineer would have written 9. On another, it completely missed the boundary cases that mattered most. On a third, it generated tests that checked whether a response arrived but said nothing meaningful about what the response contained. The tests were valid. The test suite was not useful. What we had was a fast generator with no taste, and in testing, taste is almost everything. This article is about what we learned while building toward something better: How we diagnosed what was actually wrong, why common fixes such as better prompting fall short, and what the architecture looks like when you treat test generation as a judgment problem rather than a code generation problem. The Six Ways Fast Test Generation Fails After running our pipeline across thousands of real APIs, six failure modes consistently emerged. Understanding them precisely is what pointed us toward the right solution. Over-generation is the most visible problem. The model produces 20 to 30 test cases where 8 would be sufficient. The excess cases are often plausible but redundant, and they make suites harder to review and trust. A QA engineer looking at a 30-test suite for a single endpoint will struggle to identify what is actually being covered versus what is noise. Under-generation is the …

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