Summary:
- The article explores how traditional requirements engineering methodologies can be applied to improve the accuracy and utility of AI-powered coding assistants.
- It highlights the necessity of bridging the gap between high-level project requirements and the specific code generation capabilities of Large Language Models (LLMs) to reduce technical debt and errors.
- The piece emphasizes a shift from purely model-centric development to a process-oriented framework that integrates human oversight and structured input to ensure software quality.