/context-engineering

name: context-engineering
description: Designing what information goes into the context window and in what order.

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