Analyzing the impact of prompt engineering on efficiency, code quality, and security in CRUD application development

This research evaluates the capabilities of Large Language Models (LLMs) in generating CRUD applications using Python Flask framework, focusing on code quality, security, and UI design. The study analyzes five prominent LLMs: Claude 3.5 Sonnet, Gemini, GitHub Copilot, GPT-4, and Perplexity, through automated static code review tools including Code Factor, Codacy, and Code Scene. The evaluation reveals consistently high performance across models, with GitHub Copilot and Gemini achieving superior code health metrics (9.5-10.0) and 100% green code ratings. While all models maintained Grade A ratings in Code Factor, common security vulnerabilities, particularly in Flask debug mode configuration, persisted across implementations. UI evaluation through professional interviews indicated that while generated interfaces were functionally complete, they lagged behind human-designed UIs in aesthetic appeal. The research demonstrates that LLMs can automate 60-90% of CRUD development tasks, though human intervention remains essential for environment setup and security configuration. The findings suggest a shift toward human-AI hybrid development approaches, where entry-level developers can effectively manage LLM-generated code while focusing on strategic implementation decisions.