"Walk me through an AI project you built. What architectural decisions did you make, and how did you make it production-ready?"

The capstone question — where everything from Sessions 1-10 gets tested at once. Cover all five beats in 90 seconds, or the interviewer's next question is the beat you skipped.

Miss one and they probe.

1. Problem — what the system does, who uses it, what success looks like, in one sentence
2. Architecture — the five-layer stack, what each layer does, the tools at each
3. Trade-offs — what you optimized for and what you gave up, not just what you built
4. Reliability — retry/timeout config and the one eval test that caught a real bug
5. Security — your STRIDE top-3 risks and the controls actually implemented in code

"I built a Researcher Agent — a tool-using AI system that takes a research question and returns a sourced summary, for a developer or analyst who needs fast synthesis. I optimized for answer quality over latency: Sonnet-class for synthesis, Haiku for simple lookups, accepting a 3-5 second latency target instead of sub-second. The system has five layers — a FastAPI gateway for input validation, an orchestration layer running the agent loop, a provider module wrapping the Anthropic API, a Redis cache, and a Langfuse observability layer — each one Python file, each only importing from the layer below. Every LLM call is wrapped in retry with exponential backoff, three attempts, and I run an eval harness against 10 test inputs before every merge. My top STRIDE risk was prompt injection at the gateway — I implemented input sanitisation in the Pydantic validator, stripping HTML and capping length at 2,000 characters."

Notice: every beat names something concrete and implemented — never "we plan to add that."