Optimizing LLM Prompt Engineering with DSPy Based Declarative Learning

TL;DR

This paper introduces DSPy, a declarative framework that automates and optimizes prompt engineering for LLMs, significantly enhancing accuracy, reliability, and efficiency across various NLP tasks.

Contribution

It presents a novel DSPy-based architecture combining symbolic planning and gradient-free optimization for automated prompt synthesis and correction.

Findings

Up to 45% improvement in factual accuracy.

Approximately 25% reduction in hallucination rates.

Consistent gains in output reliability and generalization.

Abstract

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. DSPy, a declarative framework for optimizing text-processing pipelines, offers an alternative approach by enabling automated, modular, and learnable prompt construction for LLM-based systems.This paper presents a systematic study of DSPy-based declarative learning for prompt optimization, with emphasis on prompt 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, improve factual grounding, and avoid unnecessary prompt complexity. Experimental evaluations conducted on reasoning tasks, retrieval-augmented generation, and multi-step chain-of-thought benchmarks demonstrate consistent gains in output reliability, efficiency, and generalization across models. The results show improvements of up to 30 to 45% in factual accuracy and a reduction of approximately 25% in hallucination rates. Finally, we outline key limitations and discuss future research directions for declarative prompt optimization frameworks.