I spent the first six months of the AI boom treating ChatGPT and Claude like a better version of Google. I would type in a problem, get a snippet of code, paste it into my IDE, and then spend twenty minutes debugging why it didn't actually work in my specific environment.
Most developers do this. We treat the prompt as a search query. But LLMs are not databases of facts. They are pattern matchers. When you ask a search engine for a solution, it finds a page where someone already solved it. When you ask an LLM, it predicts the most likely next token based on a massive pile of training data.
If your prompt is vague, the model fills the gaps with the most generic patterns it knows. That is why you get boilerplate code that looks correct but fails on the edge cases of your actual project.
The context gap
The biggest reason AI generates buggy code is the context gap. The model does not know your project structure, your specific version of a library, or your internal naming conventions.
