LLMs confidently output false information with no reliable built-in detection

Large language models frequently generate plausible-sounding but factually incorrect outputs—fabricating API functions, pricing details, or library methods—with no indication of uncertainty. Developers, knowledge workers, and teams relying on LLMs for documentation lookup, code generation, or research face a silent liability: a confident wrong answer is harder to catch than an obvious error. Current tools offer no built-in verification layer; workarounds like using one LLM to adversarially check another are manual, expensive, and still probabilistic. Retry mechanisms address failed requests but not 'successful' responses that are simply wrong, leaving teams with no reliable fidelity signal. Until hallucination detection is a first-class primitive, deploying LLMs in any critical workflow requires costly human review.