Prompt engineering alone fails in production AI systems because it's static—no clever phrasing can substitute for missing information. Context engineering is the architectural solution: dynamically assembling a 'context package' at runtime from four pillars—memory management (short-term and long-term), RAG for ground-truth retrieval, state management for multi-step workflows, and tool access via APIs. A travel agent example illustrates how injecting user location, conference details, and corporate policy into the context prevents errors like booking a hotel in Paris, Kentucky instead of Paris, France. The post also covers token cost tradeoffs, latency concerns, a prioritization hierarchy for when context exceeds the token budget, and the iterative approach to building these systems.
