Maxing out context is only useful if all the information is directly relevant and tightly scoped to the task. The model's performance tends to degrade with too much loosely related data, leading to more hallucinations and slower results. Targeted chunking and making sure context stays focused almost always yields better outcomes unless you're attempting something atypical, like analyzing an entire monorepo in one shot.