The rapid decline in AI code generation costs contrasts with rising expenses related to context engineering, crucial for correct and maintainable outputs. Current systems often mismanage retrieval quality, undermining production effectiveness. Successful AI implementations prioritize context engineering as a sustained discipline, ensuring systems adapt over time rather than merely configuring infrastructure.













