A Technical Debt-Aware Prompting Framework for Sustainable Vibe Coding: Addressing the Production Readiness Crisis in AI-Assisted Software Development

Background: Vibe coding has gained widespread adoption among developers (% adoption rate), offering intuitive AI-assisted software development through natural language interfaces. However, current practices systematically generate technical debt, creating substantial post-deployment resource burdens through performance inefficiencies, security vulnerabilities, and maintenance overhead. Objective: This working paper presents a novel Technical Debt-Aware Prompting Framework that integrates structured prompting methodologies with comprehensive context injection to transform vibe coding from a technical debt generator into a sustainable, enterprise-grade development approach. Methods: We developed an 11-domain hierarchical framework coupled with a 41- question Context Injection Questionnaire, validated against systematic literature analysis of 50 studies examining AI-assisted code generation, security integration, and performance optimization. The framework implements cross-domain validation, intelligent prompt generation, and continuous technical debt prevention mechanisms. Current Status: The framework is currently being implemented and empirically tested across multiple use cases and organizational contexts. Preliminary Results: The framework addresses critical gaps identified in current research: structured prompting shows only 13.79% improvement over traditional methods, security-focused approaches create false trade-offs with functionality, and memory management remains underexplored in 89% of studies. Our theoretical analysis indicates the framework can resolve these limitations through integrated multi-objective optimization, production-aware generation, and proactive quality assurance. Empirical validation is ongoing. Expected Contributions: Upon completion of implementation and testing, this research will demonstrate a paradigm shift from reactive technical debt management to proactive prevention, enabling organizations to leverage AI-assisted development while maintaining production standards. The framework preserves vibe coding's accessibility while embedding enterprise-grade quality considerations throughout the development lifecycle.