Stop Wasting Context

Source: Dev.to

Introduction

OpenAI says “Context is a scarce resource.”
Treat it like one.

A giant instruction file may feel safe and thorough, but it crowds out the actual task, the code, and the relevant constraints. The agent doesn’t get smarter with more text; it either:

• Misses the real constraint buried in noise
• Starts optimizing for the wrong objective
• Overfits to instructions that don’t matter right now

The Right Mental Model

Think of context like RAM in a running system:

• Finite
• Expensive
• Meant for what’s actively being processed

The same applies to LLM context.

Optimizing “RAM”

Remove

• Old decisions that no longer apply
• Duplicated instructions
• Outdated constraints
• “Nice‑to‑know” explanations

If it isn’t needed for the current task, it shouldn’t be in memory.

Don’t Preload

• All coding standards
• All architecture docs
• All squad rules

Instead

• Inject only what’s relevant to the current step
• Use smaller‑scoped agents
• Pull specific docs when needed

Context should be dynamic, not monolithic.

Structuring Instructions

Replace:

• Long paragraphs
• Repeated policy text
• Verbose explanations

With:

• Bullet‑point summaries
• Structured rules
• Canonical references

You don’t duplicate libraries in RAM—you reference them.

Example File Layout

Instead of one giant instruction file, keep separate, focused documents:

• core-standards.md
• frontend-guidelines.md
• backend-guidelines.md
• architecture‑principles.md

Load only what the current task touches.

Stable vs. Temporary Constraints

• Stable principles (coding philosophy, architectural values) should be lean and abstract.
• Temporary task constraints (fix this bug, implement this endpoint) should be precise and scoped.

Don’t mix them.

Best Practices

• Keep stable principles concise.
• Keep task context precise and scoped.
• The more constraints you add, the more the model optimizes for instruction compliance—you don’t win by giving the model everything.

Conclusion

Large context ≠ better output.

Treat context like RAM:

• Keep it lean
• Keep it current
• Load intentionally
• Evict aggressively

Systems that manage memory well perform better. Agents are no different.