context-engineering

Overview

Context is finite. What you put in the context window determines what the agent can reason about. Too much noise → the relevant signal is diluted. Too little context → the agent makes uninformed decisions. This skill manages context deliberately.

When to Use

• When an agent produces low-quality outputs despite correct instructions
• When designing a system prompt for a production agent
• When a long conversation is causing quality degradation
• When context costs are higher than expected

Process

Step 1: Define the context budget

For your model and use case: how many tokens is your budget? Reserve: 20% for the system prompt, 20% for the output, 60% for the dynamic context (documents, history, tools).

Step 2: Prioritize context by relevance

Include in this order:

1. Task instructions (always)
2. The most relevant documents or code (retrieved, not full codebase)
3. Relevant conversation history (not all history)
4. Supporting context (schemas, type definitions)

Cut: long documents that contain 1 relevant paragraph, full file contents when only a function is needed, conversation history beyond the last N relevant turns.

Step 3: Structure context for retrieval

Agents pay more attention to the beginning and end of context. Put instructions at the top. Put the most relevant context closest to the task.

Step 4: Use explicit context delimiters

Mark different sections clearly:

<system>Your role and constraints</system>
<documents>Retrieved content</documents>
<task>What to do</task>

This prevents the model from confusing instructions with retrieved data.

Step 5: Compress context aggressively

Summarize long histories. Extract the relevant portions of long documents. Use structured data (JSON, tables) instead of prose where possible.

Step 6: Monitor context quality

Track: output quality vs. context length. If longer context is producing worse results, you have a context quality problem, not a context quantity problem.