What Is Context Engineering and How to Apply It in Real Systems

An AI code assistant generates a function to handle user uploads. The code looks correct, compiles, and passes all tests. You merge it. Two days later, a high-priority bug comes in. Files from premium plan users are being processed by the standard, slower queue. The generated code called the generic function `enqueue_job()` because it had no idea that a utility `priority_enqueue_job()` existed for specific user roles. The code was correct, but wrong for the system. This is the ceiling that most teams hit with LLMs. The model’s reasoning ability is good, but it is almost completely unaware of your system’s operational reality. Fixing this requires a system-level approach. This is context engineering: the process of selecting and structuring the right information to give to the model, so the output is not just plausible, but correct within your specific environment. The cost of disconnected models When a model operates with incomplete context, it produces outputs that are plausible, but