Rule-based QA systems face significant limitations compared to LLM-based approaches. Their primary risk lies in a lack of flexibility and robustness, struggling to interpret variations in phrasing, synonyms, or complex, nuanced questions. This leads to poor handling of ambiguity and an inability to answer queries outside their exact predefined rules and patterns. Furthermore, these systems demand extensive manual effort for creation and maintenance, making them difficult to scale and update as knowledge domains evolve. They also completely lack common sense reasoning and the ability to infer answers, leading to rigid and often unsatisfactory user experiences. This ultimately results in limited knowledge coverage and a high likelihood of failing on even slightly novel or unaddressed user inputs. More details: https://www.burnet.ru/bitrix/redirect.php?goto=https://infoguide.com.ua/