Context Engineering

A brilliant prompt aimed at a clueless model produces nothing useful. Context engineering is the skill of deciding what the model needs to know and, just as critically, what it doesn't.

Supplying the right background information to an AI system, in the right format, so it can do useful work. A perfect prompt with no context produces generic output. Context engineering is the discipline of deciding what the model needs to know: relevant documents, examples of good output, constraints, domain-specific terminology, and the standards you’re working against. It’s as much about what you leave out as what you include; flooding the context window with irrelevant information degrades quality just as surely as providing too little.

Context engineering is where prompt engineering meets workflow design. In our Effective AI workshop, it’s the bridge between writing good prompts and building compounding setups. A Claude Project with well-curated context files, custom instructions, and representative examples is context engineering made durable: the context persists across conversations, improves over time, and compounds in value.

Resources

• Effective AI — teaches context engineering techniques in Session 2
• Prompt Engineering — the complementary skill of structuring the request itself
• Compounding Engineering — where context engineering becomes a system