LLM coding might seem effortless at first glance, but as Niraj highlights in his blog, it’s more complex than it looks. While large language models can generate impressive code snippets, they often lack real-world context, struggle with multi-step reasoning, and sometimes hallucinate functions or APIs. The illusion of correctness can be misleading, making debugging and integration tricky. Niraj emphasizes that successful LLM coding demands strong prompt engineering, iterative guidance, and vigilant code reviews. It’s a collaborative process—less about automation and more about augmentation. In the end, LLMs are powerful copilots, but the developer still needs to fly the plane.