Own architecture patterns for prompt structure, dynamic context assembly, retrieval-augmented generation, long-context management, conversation memory, tool context, agent state, multimodal context, source grounding, permission-aware retrieval, context compression, and context auditability. They should be able to discuss GPU memory bottlenecks, distributed inference, model-serving reliability, context quality, cost optimization, release validation, eval pipelines, observability, and production rollout with engineering teams, while also explaining architecture decisions clearly to senior leadership.