AI RESEARCH
Optimizing LLM Prompt Engineering with DSPy Based Declarative Learning
arXiv CS.LG
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ArXi:2604.04869v1 Announce Type: new Large Language Models (LLMs) have shown strong performance across a wide range of natural language processing tasks; however, their effectiveness is highly dependent on prompt design, structure, and embedded reasoning signals. Conventional prompt engineering methods largely rely on heuristic trial-and-error processes, which limits scalability, reproducibility, and generalization across tasks.