Unverified50% confidenceFactExact time
The core approach of MoPrO-SD is to decompose prompts into functional semantic modules and then sequentially refine these modules using the LLM's own feedback.
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50%
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10/12/2026
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Related Claims
UnverifiedMoPrO-SD investigates three refinement strategies: instruction-only optimization (I), instruction plus reasoning (I+R), and instruction plus per-example reasoning (I+R+E).78% similarUnverified思维链提示有效的原理是：LLM是逐词生成的自回归模型，先生成推理过程可作为后续推断的工作记忆，降低直接跳结论的错误率70% similarUnverifiedSemantics-driven LLM policies produced the strongest 'core–tail' retrieval traces, compatible with a truncated power law.70% similarUnverifiedCore LLMOps practices include prompt version management, model routing, semantic caching, observability (tools such as LangSmith, Langfuse, and Helicone), and evaluation frameworks (methods such as LLM-as-Judge).70% similarUnverifiedDSPy的核心思想是将LLM程序的结构与参数分离，由优化器自动搜索和编译生成最优的提示配置68% similar
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