LLM-Driven Microservice Evolution: Prompt-Based Feature Development and Database Adaptation

Abstract

Large Language Models (LLMs) present transformative opportunities for microservice evolution through natural language prompt interpretation. This paradigm shift enables dynamic generation of database schema modifications and API adaptors directly from business requirements, creating a more direct path between stakeholder needs and technical implementation. The architectural framework incorporates multiple layers, including an interpretation component, validation mechanisms, dynamic code generation, schema evolution management, and continuous monitoring capabilities. Prompt preprocessing significantly enhances clarity and reduces ambiguity, while the LLM layer accurately extracts intent and identifies necessary modifications. Type safety is maintained through compilation against existing systems and comprehensive validation frameworks. The semantic versioning system creates complete traceability between requirements and implementations, while automatic rollback capabilities ensure system stability. Experimental validation confirms substantial reductions in implementation time with code quality metrics comparable to traditional approaches. After optimization, performance characteristics closely approach manually written code. The presented framework indicates that the Prompt-powered microservice evolution represents a viable option for traditional development cycles, offering to improve dramatic efficiency while maintaining the necessary strength for the production environment. This advancement fundamentally changes how software systems are suitable for developing professional needs by reducing technical obstacles and accelerating convenient distribution.