Understanding Context Windows and Token Limits

Every Large Language Model (LLM) at the heart of an AI coding assistant operates within a fundamental constraint: the context window. Understanding this concept is essential for avoiding common pitfalls and getting the most out of your AI partner.

What is a Context Window?

A context window is the maximum amount of information, measured in tokens, that a model can consider at any single moment. This includes everything in your current session:

• Your prompts and instructions.
• The content of files you’ve referenced (@file).
• The conversation history.
• The code the AI generates in response.

Think of it as the model’s short-term memory. If information falls outside this window, the model can’t “see” it.

What are Tokens?

Tokens are the basic building blocks of text for an LLM. They are not words, but rather chunks of text. A single word can be one or more tokens. For example, the word “tokenization” might be split into “token”, “iz”, and “ation”.

As a rough guide, 1,000 tokens is approximately 750 words.

Why Context Windows Matter

The Power of Large Context

Modern AI assistants like Cursor and Claude Code leverage models with very large context windows (e.g., 128k to over 1 million tokens). This allows them to:

• Understand the relationships between many files in a large codebase.
• Maintain conversation history over long, complex tasks.
• Analyze entire documents or large code files at once.

The Risk of Exceeding Limits

If your session’s context exceeds the model’s limit:

• The oldest information is truncated and forgotten.
• The AI may lose track of earlier instructions or code, leading to inconsistent or incorrect output.
• Performance can degrade.

Strategies for Effective Context Management

Managing the context window is a balancing act. You need to provide enough relevant information for the AI to work effectively, but not so much that you overload it with irrelevant details.

1. Be Surgical with Context. Instead of providing entire folders, use precise @ mentions to reference specific files or symbols (@/src/api/auth.ts). This focuses the AI’s attention where it’s needed most.

2. Clear Your History. When switching between unrelated tasks, use the /clear command (in Claude Code) or start a new chat (in Cursor). This prevents context from a previous task from “leaking” into and confusing the current one.

3. Leverage Automatic Compaction. Claude Code automatically compacts long conversations to save token space. It intelligently summarizes earlier parts of the chat, retaining the most important information while discarding less relevant details. You can also trigger this manually with the /compact command.

4. Use Summarization. For very large files or long documents, ask your AI assistant to summarize the key points first. You can then use this summary as context for subsequent prompts, which is much more token-efficient.

5. Use “Max Mode” Sparingly. Cursor’s “Max Mode” unlocks the full context window of models like Gemini 2.5 Pro (1M tokens). This is incredibly powerful for analyzing massive codebases but is slower and more expensive. Reserve it for tasks that genuinely require a huge amount of context.

6. Avoid Information Overload. Don’t dump a dozen files into the prompt “just in case.” This creates noise and can confuse the model. Start with the most relevant files and add more context iteratively if needed.

By consciously managing the context window, you can ensure your AI assistant remains a focused, efficient, and powerful partner in your development workflow.