The problem is deceptively simple to describe but brutally difficult to catch in practice. An LLM returns code that compiles perfectly, generates prose that reads naturally, or produces database queries with impeccable syntax. Engineers deploy it to production. Days later, subtle logic errors surface—the function doesn't handle edge cases, the narrative contradicts itself, or the query joins the wrong tables. By then, the damage is done. This recurring gap between 'looks correct' and 'is correct' has become a defining pain point for developers building AI-powered features at scale, and it's driving urgent investment in evaluation tooling that goes beyond traditional accuracy metrics.