Enhancing Code Generation in Low-Code Platforms through Systematic Prompt Engineering

By enabling natural-language-to-code translation, recent advances in Large Language Models (LLMs) like Gemini, GPT-4, and Codex have revolutionized human-computer interaction. Despite these advancements, the quality, maintainability, and dependability of generated code are still inconsistent, primarily due to the way prompts are phrased. Prompt sensitivity becomes a major constraint in Low-Code/No-Code (LC/NC) systems, where automation and accessibility are given top priority. The literature on prompt engineering as the primary method for coordinating user intent with executable logic in AI-driven code development is compiled in this review study. It examines current frameworks for automated refining, contextual enrichment, iterative feedback, and structured prompting [1]–[5]. The study also looks at how these techniques might be integrated into LC/NC ecosystems to produce development workflows that are flexible and suitable for production. We find that systematic rapid engineering, which bridges the semantic gap between executable syntax and natural language, is the primary enabler for deterministic, high-fidelity AI code creation through comparative analysis of works published between 2023 and 2025.