Context Engineering
Teach how information is assembled, prioritized and supplied to models and agents.
Why This Matters
The model only sees what you put in front of it. Context engineering is how policy, retrieved evidence, tool results, and memory become a designed workspace rather than an unbounded prompt.
Architecture Position
Context lives in the Knowledge Layer, sitting between retrieval and the model, and is assembled again at the Runtime Layer for every tool turn. See it on the architecture map.
Start Here
Context Engineering
17 prompt and context engineering techniques for building reliable AI systems.
Embeddings
Similarity, RAG, vector search, and how meaning is represented geometrically.
Building Enterprise AI Agents
Production harness: runtime, orchestration, tools, memory, RAG, security, observability, and production controls.
Core Concepts
Fundamentals
- Context windows
- Prompt structure
- Instruction hierarchy
Context Assembly
- Dynamic context
- User context
- Application context
- Tool context
- Retrieved context
Techniques
- Few-shot examples
- Context prioritization
- Context compression
- Summarization
- Context caching
Agent Context
- Agent scratchpads
- Context for tools
- Shared context
- Multi-agent context
Security
- Context isolation
- Context poisoning
- Instruction injection
Architecture
- Context budgets
- Context pipelines
- Prompt vs context engineering
Advanced
Advanced material
Deeper guides for this domain are on the way.
Coming SoonArchitecture Patterns
Applied patterns for this domain live in the pattern library.
Browse architecture patternsCase Studies
Enterprise walkthroughs that apply this topic in a full system.
Browse case studiesRelated Topics
Models, Reasoning & Inference
How modern AI models operate and what system architects need to understand about them.
Open →Retrieval, RAG & Knowledge
How enterprise AI systems locate, rank, validate, and authorize knowledge.
Open →Memory & State
Context vs memory vs state — the most confused layer of agent architecture.
Open →Agents & Orchestration
What agents are, where they are appropriate, and how autonomous systems should be structured.
Open →Roadmaps
This topic appears in the Enterprise AI Architect path.
Follow the Enterprise AI Architect roadmap