Unverified50% confidenceOpinionExact time
The ability needed to define a problem depends on a deep understanding of business context, team capabilities, the implicit rules of legacy systems, and needs users have not yet expressed, and this information is rarely fully recorded in public texts, so LLMs cannot obtain it directly from training data.
1
Sources
50%
Confidence
Long-term
Relevance
10/13/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThese meta-skills cannot be acquired by training prompt-writing techniques, because they depend on understanding the essence of problems, which is precisely the core output of humanities training.71% similarUnverifiedCritics point out that when a problem is one for which even humans cannot provide a reference proof, the formal steps generated by LLMs still lack guidance, and the system as a whole remains unable to break through the boundaries of knowledge.70% similarUnverifiedLarge models lack a natural mechanism for distinguishing 'instruction source trustworthiness' when processing context, making it difficult to automatically identify which text constitutes authorized executable instructions and which is merely data content to be processed.70% similarUnverifiedGeneral large language models struggle to accurately reproduce company-specific implicit recruiting evaluation knowledge without targeted training or experience injection.69% similarUnverified大多数团队缺乏专为Agent设计的可观测性工具,传统日志系统记录代码执行流而无法完整记录LLM调用的prompt、推理链、工具调用参数等中间状态69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1019632API
curl https://kongchang.com/api/v1/knowledge/claims/1019632MCP
get_claim(id=1019632)