Functional Testing Alone Cannot Ensure Safe Behavior in AI Applications

As AI and large language models become embedded in real-world applications like customer support and automation, developers face a testing gap that standard methods cannot fill. Functional tests verify that an application works as expected under normal conditions, but they often miss edge cases and unexpected model behaviors. AI systems can pass all functional checks and still produce harmful, biased, or unpredictable outputs in production. This has prompted growing interest in specialized approaches such as AI red teaming and LLM-specific security testing. Developers building AI-powered products are being urged to go beyond the happy path and adopt broader behavioral evaluation frameworks.