Prompting for testing

The practice of framing instructions or context given to an AI tool in a way that shapes the quality and relevance of its output. In a testing context, how a requirement or instruction is written for an AI tool significantly affects what test cases or automation code it generates. Rather than simply asking an AI to write test cases, effective prompting names the risks to surface, specifies the types of tests needed (positive, negative, boundary), and provides enough context about the system under test for the AI to produce targeted, useful output. Poor prompting produces plentiful but shallow results; well-structured prompting produces focused, risk-aware coverage.