Context engineering is the work of giving an AI workflow the right approved information, at the right time, in a usable form. Better context does not remove the need for testing or review.
Learn the project-brief foundation in the guide
What belongs in context
- Current policies, service boundaries, and definitions.
- Relevant examples of good and bad outcomes.
- The task, constraints, and permitted actions.
- Clear ownership and a fallback for uncertainty.
What to leave out
Do not add private, outdated, irrelevant, or unverified information merely because it might be useful. Use the minimum necessary data, make permissions explicit, and keep a current source owner.
Example: inquiry response workflow
Give the workflow an approved service list, opening hours, tone examples, and a rule to route pricing exceptions to a person. It can draft a response; a staff member can verify it before sending.
Next: organize reusable content, or explore AI-agent basics.