Bridging the Prompt-Code Gap: The Evolving Role of Developers in the Age of Generative AI

Abstract

Generative Artificial Intelligence (GenAI) has rapidly transformed modern software engineering by enabling accelerated code generation, automated debugging, multi-language translation, and architectural prototyping. Despite these capabilities, a persistent disconnect remains between developer intent and AI-generated output—a phenomenon defined in this paper as the Prompt–Code Gap. This gap becomes increasingly pronounced in complex, stateful, multi-platform environments such as automated trading systems, where correctness, determinism, and system integrity are critical.

This research provides a comprehensive examination of the Prompt–Code Gap by integrating theoretical analysis with two large-scale, real-world case studies involving TradingView Pine Script strategies, PHP-based cloud routers, MQL5 Expert Advisors, and end-to-end TradingView → Cloud → MT5 execution workflows. Findings reveal that GenAI can accelerate development by up to 10×, yet its outputs frequently suffer from hallucinated functions, platform misunderstandings, regression errors, and incomplete logic—especially when handling financial signals, symbol normalization, state machines, or cross-platform trading logic.

Through an in-depth evaluation of each case study, this paper identifies key failure modes, architectural challenges, and human-AI interaction patterns that shape GenAI-assisted development. It also highlights the indispensable role of human developers in refining logic, designing system architecture, enforcing non-repainting constraints, implementing defensive programming, and integrating platform-specific behaviors. The analysis demonstrates that while GenAI produces 50–70% of scaffolding efficiently, the remaining 30–50%—including correctness, reliability, and domain-specific alignment—still requires human expertise.

This study contributes a structured conceptual framework for understanding the Prompt–Code Gap, outlines best practices for GenAI-assisted engineering, and proposes future research directions aimed at building context-aware, architecture-aligned, and platform-specific AI copilots. The paper concludes that GenAI will not replace software engineers; instead, it will amplify the capabilities of those who understand how to supervise, guide, and integrate AI-generated code into robust, real-world systems.