Many AI task failures do not happen because the model cannot modify code. They happen because the model reads the wrong context. It may trust outdated docs, treat a roadmap as fact, read an AI-assistance note as a product rule, generalize from one failed sample, or find a similarly named implementation that is already obsolete. That is why a project-specific AI delivery pipeline should not simply tell AI to "read the repository." It should prepare a context package for the task. More context is not always better It is tempting to give AI everything. In real projects, that often makes the result worse. Too much context distracts the model. Messy documentation makes temporary decisions look permanent. Long chat history can revive decisions that were already rejected. The project needs narrow context, not maximum context. Narrow context means the task receives the facts it needs, and misleading material is not exposed by default.…