Context Window Management — Strategy for Finite Memory
Even with 1M tokens, how you fill it matters
When GPT-4 had 8K tokens, what to put in context was the most critical problem. In 2026's 1M-token era, the problem hasn't disappeared — it's changed.
Why Management Still Matters
Cost — more tokens means higher API costs. Unnecessary info wastes money.
Accuracy — "Lost in the Middle" phenomenon. Models struggle to reference information in the middle of context. Important stuff goes at the beginning or end.
Speed — longer context means slower responses. Noticeable in real-time conversations.
Harness Context Management Strategies
Selective inclusion — don't dump the entire codebase. Include only files relevant to the current task. This is what Claude Code does when it explores a repo and reads only relevant files.
Summarization and compression — summarize long conversation histories to save tokens. Keep only the essence instead of full message history.
Dynamic management — needed information changes as work progresses. Files included early may be swapped out for different ones later.
Persistent storage integration — information outside the context window is stored in files, DBs, or vector stores and retrieved when needed. This is the essence of RAG.
The CLAUDE.md Pattern
Claude Code's CLAUDE.md is a good example of context management. Project rules, structure, and patterns stored in files, injected into context at each session start. Simulating the model's "long-term memory" with external files.
How It Works
Selective inclusion — only put task-relevant info in context (don't dump entire codebase)
Priority placement — important info at start/end of context, prevent Lost in the Middle
Dynamic swapping — update context contents as work progresses through stages
External storage — what doesn't fit goes in files/DB/vector stores, retrieved when needed