Applied AI / Forward Deployed Engineer · System Design for AI Applications · Lesson 1 of 2
A framework for any AI system design question
'Design an AI assistant for X' is a staple round. Don't jump to a diagram — run a structured framework that shows product + engineering + AI judgment.
- Clarify requirements: users, scale (QPS, docs), latency budget, accuracy bar, budget, data sources, offline/online, what 'good' means. Always start here.
- Define success + evals: how you'll measure quality before you build. Signals matter more than architecture.
- High-level architecture: ingestion/indexing → retrieval → orchestration (prompt/agent) → model → post-processing/validation → UI. Draw the data flow.
- The AI core: prompt vs RAG vs agent vs fine-tune; model choice; context strategy; structured output.
- Production concerns: latency (streaming, caching, routing), cost, reliability (retries/fallbacks), security (injection, PII), observability.
- Feedback loop: capture user signals + logs → grow eval set → improve. Close the loop.
- Tradeoffs: state them out loud (build vs buy, quality vs cost vs latency). Seniority = naming tradeoffs, not reciting components.
Two moves that win this round1) Start by clarifying — never architect a system you haven't scoped. 2) Bring up evals and the feedback loop unprompted — most candidates forget, and it instantly signals applied maturity.
◆ Lock it in
- Run the framework: clarify → evals → architecture → AI core → production → feedback loop → tradeoffs.
- Clarify first; quantify scale, latency, accuracy, and cost budgets.
- Volunteer evals + feedback loop + explicit tradeoffs to signal seniority.
Feynman drill — say it out loudRecite the 7-step AI system design framework from memory.
Step 1 rate your confidence · Step 2 pick your answer
You're asked to 'design an AI assistant that answers questions over a company's documents.' Your first move should be:
Step 1 — how sure are you?