Building trust in AI tools: Why 'my prompt was good' is not enough

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A software architect developed an AI-powered tool to audit authentication system implementations in production codebases, but quickly realized he could not confidently vouch for its reliability. When tested against real codebases, the tool was caught making confident but incorrect claims — for instance, misreporting token storage defaults because it searched for a v6 library pattern in a codebase running v5. The tool also produced inconsistent results across multiple runs and when switching between AI models, raising concerns about repeatability. To address this, the developer built adversarial test fixtures to evaluate recall, precision, generalization, and edge-case handling, then iterated on the tool based on findings. The article argues that any AI tool making claims about checkable sources — codebases, documents, or datasets — requires structured, evidence-backed testing rather than relying on prompt quality alone.

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