Context Engineering¶

Platforms: claude openai gemini m365-copilot

What Context Engineering Is¶

Context engineering is the practice of designing and optimizing everything an AI model sees when it generates a response. This goes well beyond writing a good prompt — it includes system prompts, instructions, user input handling, tool integrations, retrieved knowledge, memory, and state management.

The term emerged as the industry recognized that a well-structured context makes even a simple prompt produce excellent results, while a perfectly crafted prompt with poor context still produces poor results. The focus shifted from how you ask to what information the model sees.

How It Extends Prompt Engineering¶

Prompt engineering focuses on crafting the input text — choosing the right words, structure, and examples to get better outputs from a model. It's an important skill, but it's one component of a larger system.

Context engineering takes a holistic view of the entire context window. It treats all the elements that influence a model's behavior as interconnected components that need to be designed together:

Component	Prompt Engineering	Context Engineering
Scope	The text of the prompt itself	Everything the model sees: system prompt, instructions, user input, tools, retrieved data, memory, state
Focus	Word choice, structure, examples	Architecture of the information environment
Optimization	Iterate on prompt wording	Design how components work together
Persistence	Per-conversation	Across sessions, users, and workflows

Prompt engineering is a subset of context engineering — an essential one, but not the whole picture.

The Components¶

Context engineering orchestrates multiple components that together shape model behavior. Each maps to one or more building blocks:

System Prompts¶

The foundational layer that establishes the AI's role, capabilities, and behavioral parameters. System prompts define who the AI is for a given workflow — its expertise, tone, boundaries, and default behaviors.

Effective system prompts include role definition, behavioral guidelines, and capability boundaries. They can incorporate conditional behavior (adapting based on context), persona switching, and output formatting standards.

Building blocks: Prompts, Projects

Instructions¶

Specific guidance for how the AI handles different types of tasks. While system prompts set the overall character, instructions direct behavior for particular scenarios — analysis tasks, creative work, technical tasks, and more.

Well-designed instructions include priority hierarchies (what takes precedence when guidance conflicts), escalation procedures (what to do when the AI can't complete a request), and dynamic adaptation (adjusting depth based on user responses).

Building blocks: Prompts, Skills

User Input Processing¶

How the system interprets and enriches what users provide. This includes intent recognition (identifying what the user actually wants), context extraction (pulling implicit information from messages), and ambiguity resolution (strategies for handling unclear requests).

Building blocks: Prompts

Structured Inputs and Outputs¶

Defining precise formats for complex requests and responses. JSON schemas, templates, and metadata structures make interactions more predictable and consistent. On the output side, standardized formats and progressive disclosure (layered detail levels) improve usability.

Building blocks: Prompts, Skills

Tools¶

Integrations that extend AI capabilities beyond text generation. Context engineering considers which tools are available, how they're described to the model, and how their outputs are integrated back into the context. Tool categories include information retrieval, computation, communication, and content creation.

Building blocks: MCP, Agents

RAG and Memory¶

Systems for accessing external knowledge and maintaining information across interactions. Retrieval-Augmented Generation (RAG) — retrieving relevant documents from a database and injecting them into the prompt — is one approach. Memory systems add persistence: short-term memory within a conversation, and long-term memory across sessions.

This includes decisions about what to retrieve, how to rank results, and what to remember versus forget.

Building blocks: Context, Projects

State and Historical Context¶

Tracking the current status of interactions and learning from past ones. Conversation state (current topic, goals, progress), user state (expertise level, preferences), and task state (completed steps, blockers) all shape how the AI responds.

Historical context adds pattern recognition — identifying recurring themes, successful approaches, and seasonal patterns that inform future interactions.

Building blocks: Context, Projects, Agents

Why Context Engineering Matters¶

Context engineering addresses what happens when AI moves from simple chat interactions to production systems:

Consistency — Designed context produces reliable, repeatable results across users and sessions
Scalability — Systems that assemble context programmatically scale in ways that manual prompt crafting cannot
Maintainability — Separating components (system prompt, instructions, tools, memory) makes systems easier to update and debug
Quality ceiling — The upper bound of AI output quality is set by the context, not the prompt. Better context engineering raises that ceiling.

Relationship to Context Graphs¶

Context graphs are a specific technique within context engineering. Where context engineering is the overall discipline of designing what the model sees, context graphs are a structured approach to organizing relationships, decisions, and reasoning chains in that context.

Think of it this way: context engineering is the practice; context graphs are one of the more advanced tools in that practice.