arXiv:2605.26898v1 Announce Type: cross Abstract: Large Language Models (LLMs) can generate functional source code from natural-language prompts, but often fail to consistently follow higher-level architectural structures or design patterns. Since LLMs are increasingly used in software engineering, their ability to apply established design principles to generated code is crucial to the long-term success of software products. Therefore, the goal of this paper is to identify strategies for guiding LLMs to incorporate design patterns into the generated source code. We designed a computational exp
The rapid advancement and widespread adoption of large language models in software development necessitate strategies to ensure generated code adheres to established design principles.
The ability to guide LLMs towards structured and maintainable code is critical for the scalability, reliability, and long-term success of software projects incorporating AI-generated components.
This research outlines actionable strategies for developers to improve the structural quality and adherence to design patterns of LLM-generated code.
- · Software engineers
- · AI model developers
- · Software development companies
- · Businesses adopting AI for software generation
- · Companies relying on unstructured or poorly designed AI-generated code
Increased reliability and maintainability of AI-generated software components will accelerate their integration into production systems.
The development of more sophisticated prompt engineering and fine-tuning techniques specifically for software architecture will become a key competitive advantage.
This could lead to a redefinition of software architecture roles, shifting focus from manual pattern application to guiding and validating AI synthesis.
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Read at arXiv cs.AI