Learn from the Constraint

At the time of writing (June 2025), context windows of ~200K tokens are viewed as a negative technical constraint. This need not be the case: the constraint becomes the training ground for developing the skills to efficiently wield larger contexts.

The Training Effect

Working within token limits forces deliberate choices about what to include, how to structure information, and when to start fresh. Like optimizing code for slower hardware, these constraints develop fundamental skills.

Without limits:

• Dump entire codebases into context without curation
• Include tangential information "just in case"
• Rely on the model to sort through noise and irrelevant details
• Write vague prompts expecting the model to figure out intent

With constraints:

• Explicit file selection - Deliberately include/exclude specific files based on relevance
• Clear task definition - Break down objectives into concrete, actionable steps
• Context-sized chunking - Divide large tasks into pieces that fit within token limits
• Modular refactoring - Structure code into lean, focused modules that can be selectively read
• Compact examples - Provide minimal but representative examples for the model to learn patterns
• Precise prompting - Write targeted requests that specify exactly what's needed and in what order
• Priority-based organization - Structure information with most critical details first

Performance Degradation

As context windows fill up, LLM performance actually decreases. Models become less precise, are more prone to error, and struggle with complex reasoning when operating near token limits.

The goal becomes providing the minimum context necessary to execute the task effectively. This approach maximizes performance, token efficiency, and cost efficiency simultaneously.

Like optimizing an algorithm for better time complexity, you're eliminating unnecessary operations by reducing informational overhead while maintaining the same effective output.

Skills That Scale

Token constraints teach you to:

• Identify essential context while aggressively filtering out irrelevant details
• Utilize CLAUDE.md to get better results
• Understand how different pieces of information connect and depend on each other
• Distinguish between project-specific context and general knowledge the model already possesses
• Choose examples that efficiently demonstrate patterns rather than exhaustively covering cases

These skills make you more effective even with unlimited context.

The Paradox

Developers who learn with unlimited context may develop inefficient habits. Those who embrace constraints become better collaborators regardless of context size.

Working within limits teaches the fundamentals that scale beyond any technical constraint.

See Also: Context Window Depletion