AI-driven features introduce risks traditional QA doesn't catch unreliable model outputs, prompt injection, unvalidated inputs reaching inference endpoints, and no clear fallback when the model gets it wrong.
AI features fail differently than traditional software. A model can be technically "working" and still return an unreliable, manipulated, or nonsensical result and standard QA processes, built around deterministic outputs, often don't catch it. Left unaddressed, that risk reaches production.
Inputs reaching an inference endpoint are validated and sanitized the same way any other untrusted input would be, closing off prompt injection and malformed-input failure modes.
Model endpoints are authenticated, rate-limited, and monitored like any other production service not left open as an unmanaged integration point.
Low-confidence or ambiguous model outputs are flagged for review rather than silently presented as fact, so failure is visible instead of hidden.
Before shipping, we test with inputs designed to break the model malformed data, edge cases, and adversarial prompts not just the happy path.
AI features are integrated with the same engineering discipline as any other critical system component because in production, "the model usually works" isn't good enough.