Coding with LLMs sounds deceptively simple, but the reality is more complex than YouTube demos suggest. This post explores the challenges of
While coding with large language models (LLMs) seems magical, the reality is more nuanced. In his blog, Niraj explores the hidden complexities of using LLMs for software development. He highlights how LLMs often produce plausible-sounding but incorrect code, struggle with multi-step logic, and lack true understanding of the broader context. The article underscores the importance of strong prompt engineering, iterative feedback, and human oversight. Niraj also points out that LLMs can hallucinate APIs or make unsafe assumptions, making debugging harder. While they can be powerful accelerators, they're not replacements for thoughtful engineering. Ultimately, coding with LLMs is a new skillset—one that requires both caution and creativity.
