Search Results: LLMLimitations

Large Language Models generate functional code for common scenarios but consistently miss edge cases, revealing fundamental limitations in their reasoning capabilities. This article examines why pattern-matching AI struggles with boundary conditions and explores emerging solutions that combine LLMs with systematic testing frameworks.

Despite bold promises from tech giants like OpenAI and Google, AI agents—touted as autonomous assistants for tasks like coding and finance—are failing at alarming rates due to hallucinations and compounding errors. Industry benchmarks reveal failure rates up to 70%, exposing critical vulnerabilities and technical debt that undermine their reliability. This disconnect highlights fundamental flaws in the LLM-driven approach and signals an urgent need for alternative AI strategies.