LLMs เข้าใจและเขียนโค้ดได้อย่างไร?

The article explores how large language models (LLMs) manage to understand and generate code without truly "thinking" like humans. It explains that LLMs don't genuinely comprehend code but instead recognize patterns from vast amounts of data they've been trained on, like millions of lines of code from GitHub and Stack Overflow. This means LLMs essentially mimic human-like thinking through pattern recognition rather than true comprehension. This insight is crucial as it sheds light on the capabilities and limitations of AI in programming, helping developers understand how best to leverage these tools.