Context Engineering vs Prompt Engineering: the shift in how we build AI systems.

You receive a pull request coming from an AI. The code looks clean, follows the prompt, and all unit tests pass. Then you notice it uses a library the team deprecated last quarter. And a design pattern that violates the service’s architecture document. The code looks right, but it is completely wrong in the context of your system. This is the absolute limit of prompt engineering. The whole "context engineering vs. prompt engineering debate" comes down to a practical problem: our AI tools keep delivering code that requires careful manual fixes. Prompt engineering works well for isolated tasks. Building software is not a sequence of isolated tasks. It is a chain of decisions constrained by existing code, team habits, and business rules. The problem is not a poorly written prompt. The problem is that the model has no idea what is happening outside its small window. Asking a better question does not help when the model cannot see the rest of the codebase. The costs of prompt engineering at