Optimizing LLM Prompt Engineering With DSPy Based Declarative Learning > 공지사항

Abstract:Large Language Models (LLMs) have proven sturdy performance throughout a wide range of natural language processing duties; nevertheless, their effectiveness is very dependent on immediate design, construction, and embedded reasoning alerts. Conventional immediate engineering strategies largely rely on heuristic trial-and-error processes, which limits scalability, reproducibility, and generalization throughout tasks. DSPy, a declarative framework for optimizing textual content-processing pipelines, presents an alternate approach by enabling automated, modular, and learnable prompt development for LLM-primarily based this http URL paper presents a systematic examine of DSPy-primarily based declarative learning for prompt optimization, agreement east rap with emphasis on immediate synthesis, correction, calibration, and adaptive reasoning control. We introduce a unified DSPy LLM architecture that combines symbolic planning, gradient free optimization, and automated module rewriting to reduce hallucinations, enhance factual grounding, and keep away from pointless prompt complexity. Experimental evaluations carried out on reasoning tasks, retrieval-augmented technology, and multi-step chain-of-thought benchmarks reveal constant beneficial properties in output reliability, efficiency, and generalization throughout models. The results present enhancements of up to 30 to 45% in factual accuracy and a discount of approximately 25% in hallucination charges. Finally, we outline key limitations and discuss future analysis directions for declarative immediate optimization frameworks.

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