Context Engineering

Context Engineering

The context window is finite. What goes into it — and in what order — determines the quality of every output. Context engineering is the practice of deliberately designing the information architecture of the context window.

The Context Budget

Every context window has a token budget. Allocate it deliberately:

• System prompt: The foundational instructions (typically 5-20% of the budget)
• Retrieved context: Documents, data, and information pulled in for the current task
• Conversation history: Previous turns in the conversation
• User input: The current request
• Working space: Room for the model to generate its response

These compete for space. More retrieved context means less conversation history. A longer system prompt means less room for everything else.

Information Architecture in Context

Order matters. The model pays different amounts of attention to different positions:

• Beginning: High attention. Put your most important instructions here.
• Middle: Lower attention. This is where information can get lost in long contexts.
• End: High attention. The most recent information (user input) naturally goes here.
• Adjacent to the task: Information placed right before the user's question gets more attention than information earlier in the context.

Context Selection

Not everything should go into the context. Design selection criteria:

• Relevance: Does this information help answer the current question?
• Recency: Is this the most up-to-date information available?
• Specificity: Is this specific enough to be useful, or is it too generic?
• Redundancy: Is this information already covered elsewhere in the context?
• Authority: Is this from a reliable source?

Context Strategies

• Retrieval-augmented generation (RAG): Pull relevant documents into the context dynamically
• Summarisation: Compress older context into summaries to free up space
• Prioritised history: Keep recent and important conversation turns, drop less important ones
• Structured context: Organise information with clear headers and sections so the model can navigate it
• Context caching: Pre-compute and cache frequently used context blocks

Context Quality Signals

How to tell if your context engineering is working:

• Output relevance: Do outputs address the actual question using the provided context?
• Hallucination rate: Is the model making things up because the context is insufficient?
• Context utilisation: Is the model actually using the provided context, or ignoring it?
• Consistency: Are outputs consistent when the same context is provided?

Design Artefacts

• Context budget allocation documents
• Information architecture diagrams for the context window
• Context selection criteria per feature
• Retrieval strategy specifications
• Context quality monitoring metrics

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Owl-Listener
• Source: Owl-Listener/ai-design-skills
• License: MIT

Install and usage instructions live in the source repository linked above.