LLMs Are Tools, Not Replacements (rselbach.com)

A recent post emphasizes that large language models (LLMs) should be viewed as tools rather than replacements for human developers. The author, initially skeptical about LLMs, shares their journey of gradually incorporating these models into their workflow, particularly for coding and debugging. They detail a critical moment when an LLM helped identify a complex issue in their code that they had previously overlooked, which led them to reconsider how to best utilize these technologies. This shift in perspective is significant for the AI/ML community, highlighting the evolving role of LLMs as collaborative partners rather than standalone solutions. The author shares a structured workflow that involves phases of problem understanding, planning, implementation, and rigorous review, ensuring that LLM contributions are constantly vetted and contextually relevant. They also caution against over-reliance on LLMs for tasks like automated testing or complex refactoring, suggesting that while LLMs enhance productivity, human oversight remains critical to maintain code quality and decision-making. This recognition promotes a balanced approach to AI in software development, advocating for synergy between human intuition and machine efficiency.