Unverified50% confidenceFactExact time
由于LLM的输出具有不确定性，工作流设计需要考虑提示词的鲁棒性和输出格式的可解析性
1
Sources
50%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
AI Agent开发入门：掌握核心思维，轻松跨越零基础门槛
bilibiliAI应用全栈开发7/11/2026
Related Claims
UnverifiedLLM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77% similarUnverifiedEngineers can address LLM output non-determinism by introducing output validation, retry mechanisms, and structured output constraints (such as Function Calling or JSON Mode).75% similarUnverifiedDifferent LLM models vary in their accuracy of understanding instructions; it is recommended to iteratively optimize prompts multiple times to achieve the best results.74% similarUnverified对于语义解析和意图路由需求，应让LLM负责语义映射，而将几何计算交给专业库执行，不应让LLM直接做精确浮点运算74% similarUnverified与 Airflow、Temporal 等工业级工作流引擎不同，LangGraph 专为 LLM 输出的不确定性设计，条件路由可基于模型返回的自然语言内容73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/501840API
curl https://kongchang.com/api/v1/knowledge/claims/501840MCP
get_claim(id=501840)