Unverified75% confidenceOpinionExact time
LLMs can handle ambiguous, unstructured work requiring judgment, unlike traditional automation which only handles repetitive rule-based tasks
1
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75%
Confidence
Long-term
Relevance
8/2/2026
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UnverifiedLLM的职责是理解意图、选择合适函数、用自然语言解释结果，业务规则和模型推理应交给Function，不应让LLM直接替代专业判断72% similarUnverifiedLLM在处理需要全局架构理解、跨文件依赖推理、复杂业务约束建模等深度理解型任务时仍面临显著局限72% similarUnverifiedEarly LLM applications were mostly single-turn input-output patterns where prompt tuning could solve most problems, but Agentic workflows have made application logic increasingly complex71% similarUnverifiedLLM在「生成」和「评估」时激活的推理路径不同，生成任务偏向延续已有语义方向，评估任务激活更多批判性、面向异常检测的推理路径71% similarUnverifiedLLM在需求描述模糊时容易过度设计，倾向于生成复杂的企业级架构方案，这源于其基于条件概率的文本续写特性而非工程判断69% similar
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