Master the full discipline of designing what an AI model sees. Prompt engineering is one slice — context engineering is the full stack that determines whether your AI system actually works in production.
Context engineering is the discipline of designing everything the AI model sees — system prompt, conversation history, retrieved documents, tool definitions, and tool outputs. Prompt engineering is a subset focused on wording individual messages. Context engineering governs the full package of information the model conditions on, which is why it has a much larger impact on production AI system quality.
The context engineering pathway contains 12 articles and takes approximately 6 hours to complete at a comfortable reading pace. You can progress at your own speed — most practitioners complete it over 1-2 weeks alongside their regular work.
The early articles (what context engineering is, how context windows work, the distinction from prompt engineering) require no coding background. Articles on RAG pipeline design, tool schema design, and agentic context design include code examples and are better suited to developers. The pathway is structured so you can stop at the level that matches your role.
Understand what AI actually is — tokens, transformers, agents, and the landscape. Start here if you're new.
11 articles · ~4h →Go from vague requests to precise, reproducible AI outputs. The skill that underpins everything.
13 articles · ~5h →Go from zero to productive with Claude Code — the terminal AI coding agent that ships real projects.
15 articles · ~7h →What Is Context Engineering?
The full-stack discipline of assembling everything the model sees — from RAG to tool schemas to history.
LLM Context Window Explained
What a context window is, how it differs from parameter count, and 2026 model comparisons.
Context Engineering vs Prompt Engineering
The precise distinction: prompt engineering fixes your wording; context engineering designs what the model sees.
Context vs Prompt vs Loop vs Harness Engineering
Four layers of the agent stack — how they nest, what breaks when you skip one, and which lever to fix when agents fail.
RAG and Context Injection: Pipeline Design
RAG is a context engineering problem — how to chunk, retrieve, score, and inject for maximum effectiveness.
Tool Definition and Schema Design
The context engineering layer most teams get wrong — how to write tool schemas that produce reliable agent behavior.
Conversation History Management
What to keep, compress, and drop — four strategies for managing history in multi-turn agent sessions.
Token Budget Planning and Execution
How to allocate, monitor, and optimize token budgets across context window components.
Prompt Caching for Context Engineers
Cache stable context prefixes to cut LLM costs by 50-80% without changing model behavior.
Context Compression with Headroom
Keep agents effective even when context windows fill — context compression strategies.
Agentic Context Design
How to engineer the evolving context window for multi-turn, multi-step AI agent systems — the capstone.
Measuring Context Quality
Build eval sets, run A/B tests, and measure what actually matters for context quality.
Yes. Even for single-turn AI applications, context engineering principles apply: what you include in the system prompt, how you structure retrieved documents, and what constraints you specify all affect output quality. The impact compounds in agentic systems, but the foundations are valuable for anyone building with LLMs.
Understand and build the loops, harnesses, and protocols that make AI agents reliable and autonomous.
16 articles · ~6h →
