I'm not a good enough engineer to code with LLMs (kian.wtf)

A recent reflection on the experience of using large language models (LLMs) for coding reveals both the allure and pitfalls of AI-assisted development. The author initially embraced LLMs for tasks like data visualization, where the technology seamlessly translated descriptions into visuals. However, as they delved deeper into more complex coding tasks, they found themselves caught in a cycle of dependency, akin to gambling, repeatedly seeking out one-shot solutions rather than engaging in the thoughtful problem-solving process inherent to traditional programming. This resulted in a dissonance between the instant gratification of successful outputs and the deep understanding that usually comes from structured programming. The significance of this narrative lies in its exploration of the limitations of LLMs in software engineering. While these models excel in generating code snippets, they may obscure the process of hierarchical problem-solving, which is crucial for more intricate tasks. The author’s decision to refrain from directly using LLM-generated code in their projects highlights a critical takeaway for the AI/ML community: as LLMs become more integrated into the coding landscape, engineers must maintain a balance between leveraging AI for efficiency and preserving the foundational learning processes that contribute to effective software development.