Enhancing programming productivity through domain specific code generation with large language models

• DOI: 10.63345/ijrsml.v13.i3.7
• Publication Date: 2025-03-27T08:08:37Z
• Authors: Aatishkumar Dhami; Dr Munish Kumar
• Keywords: Domain-specific language; Code (set theory)

This paper investigates the potential of large language models (LLMs) to enhance programming productivity through domain-specific code generation. By integrating domain expertise with advanced machine learning techniques, our approach tailors LLMs generate that aligns closely specialized application requirements. The study outlines a systematic framework for fine-tuning using datasets, enabling automated synthesis accurate and efficient code. Experimental results demonstrate this targeted methodology not only reduces development time mitigates common coding errors but also improves overall maintainability. Comparisons traditional practices reveal domain-adapted provide significant gains in both speed reliability. insights derived from research contribute broader understanding how artificial intelligence can be leveraged streamline software processes, ultimately offering robust toolset modern developers.