llm/065c6e83-d0d5-4aca-be3d-92768a8a3506/topic-16-4a42dc3e-c300-4552-b11b-6036f3a5d6e0-output.json
While AI tools can dramatically accelerate greenfield development by compressing months of work into days, their efficacy in complex legacy systems remains a point of contention among developers. Some argue that LLMs struggle with the nuanced requirements of massive, distributed architectures, while others find success by ensuring codebases remain modular and free of "godclasses" that overwhelm model context. To bridge this gap, many practitioners are shifting toward formal "planning modes" and manual context-management strategies, such as maintaining subsystem markdown files to guide the AI through large-scale projects. Ultimately, the consensus suggests that the true test of AI coding lies not in its initial output, but in its long-term ability to maintain and scale professional software over years of evolving requirements.