Managing AI context effectively is no longer just about technical optimization; it is the fundamental constraint on model performance and cost-efficiency in modern development workflows. The central thesis is that Claude's context window acts as a finite working memory that degrades in performance, a phenomenon known as 'context rot,' as the session grows. Because models re-read the entire conversation history with every new message, token usage grows exponentially rather than linearly, turning long-running sessions into costly, inefficient cycles of redundant processing. The industry-standard '1 million token' context window serves better as an insurance policy than a target to fill, as retrieval accuracy measurably declines as the window reaches capacity. To combat this, power users must shift from passive interaction to active session management. By employing strategies like manual compaction, leveraging sub-agents to silo specific tasks, and converting rich file formats to lean markdown, users can significantly reduce overhead. The most effective approach involves a 'session chaining' workflow where distinct project phases—discovery, planning, and execution—occur in separate, focused context windows. Relying on auto-compaction is a strategic error, as it kicks in too late when the model is already distracted, discarding valuable information indiscriminately. Ultimately, successful AI orchestration requires the listener to cultivate discipline: treating the context window as a limited resource and proactively resetting sessions with summarized state transfers to maintain high performance and lower costs.