How One Developer Built a 6-Case Test Harness to Safely Swap AI Providers in a Hiring SaaS

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A solo SaaS developer building an AI-powered candidate scoring tool argues that sharing a common API interface across OpenAI, Claude, and Gemini does not guarantee consistent scoring behavior. To manage this, they designed a versioned scoring contract backed by six acceptance test cases that any provider-model pair must pass before being promoted to production. The system uses a domain-specific scoreCandidate() abstraction rather than a generic chat method, preserving a clear boundary between untrusted resume input and downstream hiring decisions. Key design choices include local computation of weighted scores, strict schema validation on every response, and treating missing evidence as null rather than zero to avoid false precision. The developer also separates batch processing from interactive calls, and routes failed responses to manual review rather than silently defaulting them to a passing score.

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