This sounds approachable until you're actually in it.
Start by clarifying requirements (e.g., scale, latency, use cases) and then outline a high-level architecture covering data ingestion, model serving, and user interaction. Dive into key components like the LLM, retrieval-augmented generation, and safety layers, discussing trade-offs at each step. Conclude by addressing scalability, monitoring, and iteration.
Pro tip: Emphasize the importance of evaluation and feedback loops—OpenAI values systems that continuously improve through user interactions and safety checks. Also, be explicit about trade-offs between latency, cost, and quality, as these are critical in production AI systems.
Ask about expected scale (users, queries per second), latency requirements, use cases (e.g., customer support, general assistant), and constraints (budget, compliance). This ensures the design meets actual needs.
Sketch the main components: client interface, API gateway, orchestration layer, LLM inference service, retrieval system (if needed), and data stores. Explain how they interact.
Discuss the choice of LLM (e.g., GPT-4), fine-tuning vs. prompting, retrieval-augmented generation for knowledge grounding, and safety filters. Address trade-offs like cost vs. performance.
Explain how to handle high traffic (caching, load balancing, autoscaling), ensure low latency (model optimization, edge deployment), and maintain uptime (redundancy, failover).
Describe logging, metrics (e.g., response quality, latency), A/B testing, and user feedback loops to continuously improve the system and address safety concerns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.