Embedding Traceability in Large Language Model Code Generation: Towards Trustworthy AI-Augmented Software Engineering

The rise of LLMs in software engineering introduces critical challenges in tracing generated code to its governing requirements. Traditional traceability methods, designed for human-authored code, fail to address LLMs' stochastic and opaque nature, which yields divergent outputs from identical prompts. This paper proposes embedding traceability as a first-class objective in LLM code-generation pipelines through three strategies: structured requirement prompting, metadata-aware fine-tuning, and retrieval-augmented validation. Our framework addresses LLM opacity, non-determinism, and scalability, while introducing metrics, dynamic adaptation, and ethical considerations for audit-ready code generation. Contributions include an end-to-end framework, a research agenda, and design principles to support trustworthy AI-augmented software engineering.