Context Engineering Fundamentals
Understanding the shift from single prompts to dynamic, intelligent systems
What is Context Engineering?
"Context engineering is designing and building dynamic systems that give an LM the right info in the right format at the right time to accomplish a task."
The foundational definition of context engineering
"The LM is the CPU and the context window is the RAM"
— Andrej Karpathy
NOT Context Engineering
Casual chatbot conversations:
- • "What running shoes should I buy?"
- • Back-and-forth conversations
- • Iterative prompt refinement
- • Single-use interactions
IS Context Engineering
Building AI applications & agents:
- • Customer service AI agents
- • Complex prompt systems
- • Multi-tool integrations
- • Enterprise AI applications
The 6 Essential AI Agent Components
The AI Agent "Burger" Analogy
Just like a burger needs a bun, patty, vegetables, and condiments to be called a burger, every AI agent needs these 6 essential components to function properly.
Context engineering is creating the "instruction manual" that tells the agent how to use all these components together.
Model
The AI brain - GPT, Claude, Gemini, or open source models. The core intelligence that processes context and generates responses.
Tools
External system integrations - APIs, calendars, databases, web search. Enable agents to interact with the world beyond conversation.
Knowledge & Memory
Information storage and retrieval - conversation history, knowledge bases, documents. Enables contextual understanding across interactions.
Audio & Speech
Voice interaction capabilities - speech-to-text, text-to-speech. Makes agents more natural and accessible for user interactions.
Guardrails
Safety and behavior constraints - content filtering, appropriate responses, ethical boundaries. Ensures responsible AI behavior.
Orchestration
Deployment and monitoring systems - hosting, performance tracking, improvement loops. Keeps agents running and improving over time.
The Context Engineer's Role
You create the "instruction manual" that tells the agent exactly how to use all these components together.
This results in complex, structured prompts that detail tool usage, memory access, knowledge base queries, speech interactions, safety constraints, and orchestration flows - far beyond simple prompt engineering.