A Framework for AI Design Answers

Strong AI design answers have a shape. They are not the ones that name the most technologies. They are the ones that walk through the problem in an order that lets the interviewer, or a teammate, follow, and that leave no major area untouched. This chapter gives you that order and a way to check your own answers against it.

Pilots do not rely on memory for what to check before takeoff. They run a checklist, and it is not because they are beginners. It is because the cost of skipping one step is high and a checklist makes skipping hard. A design framework works the same way: it does not limit your thinking, it protects you from forgetting evaluation, failure modes, or cost when the conversation gets interesting.

• -Clarify: users, scale, goal, constraints, what happens when it is wrong.
• -Frame: the business goal, the metric that measures it, and the task the system performs.
• -Data and knowledge: what data or documents exist, how they are labeled or kept fresh, who may see what.
• -Approach: start from a baseline, then the architecture, with the main components and data flow.
• -Evaluate: offline measures, an eval set, and an online experiment, with guardrail metrics.
• -Serve: latency, throughput, cost, scaling, and fallbacks.
• -Safety and failure modes: what can go wrong, abuse, privacy, and how each is contained.
• -Monitor and iterate: what you watch after launch, how you retrain or update, and what you would build next.

• -Classic ML: step 3 is labels and features, step 4 is the retrieval and ranking funnel, step 5 is offline metrics and an A/B test, step 7 is leakage, bias, and feedback loops.
• -GenAI: step 3 is the corpus and its freshness and permissions, step 4 is context assembly, retrieval, model choice, and prompts, step 5 is the eval set and judges, step 7 is hallucination, prompt injection, and data leakage.
• -Agentic: step 3 is the tools and the data they touch, step 4 is the control loop and its limits, step 5 is trajectory and outcome evals, step 7 is permissions, approval, loops, and injected instructions.

• -Jumping to a model or a diagram before clarifying. You will design for a problem you invented.
• -Skipping the baseline. A model that cannot beat a simple rule is not worth running.
• -Describing evaluation as an afterthought, or not at all, or naming only accuracy.
• -Ignoring cost and latency until the end, when the design may no longer fit.
• -Leaving out failure modes, drift, and monitoring, which is where real systems spend their lives.
• -Going deep on one box and leaving the rest blank. Breadth with a few strong details beats depth in one area.

A rubric is a list of what a complete answer touches. The code defines ten items and scores two sample answers. The first is detailed about architecture and cost but skips most of the rest. The second touches nine of the ten. Press Run, and look at what each one is missing.

Answer A covers 3 of 10, 30%. It has an architecture, data, and cost, but it never clarified the goal, never stated a metric or a baseline, and has no evaluation, safety, or monitoring. Answer B covers 9 of 10, 90%, and its one gap, safety and failure modes, is easy to spot and fix. You can run this check on your own practice answers: write down what you said, tag it, and see what you left out.