Context Management¶
Context management refers to the strategies and techniques for working within the limited context window of large language models. Since LLMs can only process a finite amount of text at once (typically 100K-200K tokens), researchers must be deliberate about what information the model sees at each step.
Context & Background¶
Every LLM interaction is constrained by a context window — the total amount of text (prompt + conversation history + response) the model can handle. For long research tasks, this creates practical challenges: the model may "forget" earlier instructions, lose track of project goals, or fail to connect insights across different parts of a large codebase.
Effective context management strategies include:
- CLAUDE.md files: Persistent instructions loaded at the start of every session
- Structured project files: Keeping plans, progress notes, and key decisions in files the AI can reference
- Chunking: Breaking large tasks into focused sub-tasks that fit within context
- Summarization: Periodically summarizing progress to compress context
- Memory files: Storing key decisions and learnings for retrieval across sessions
Practical Implications¶
- Front-load critical information: Put the most important instructions at the beginning
- Use file-based memory: Don't rely on conversation history alone — write important context to files
- Be explicit about scope: Tell the model exactly which files or data to focus on
- Refresh context periodically: In long sessions, re-state key goals and constraints