1. Gather context about current AI usage and symptoms.
2. Diagnose context stuffing symptoms.
3. Ask diagnostic questions to evaluate context relevance and necessity.
4. Define memory architecture (short-term vs. long-term).
5. Implement the Research → Plan → Reset → Implement cycle.
6. Create an action plan with next steps.
7. Review and iterate on the context engineering process.