The Four Recurring Patterns
Explore how to quickly recognize four recurring patterns in generative AI system design interviews: RAG orchestration, feedback loops, hallucination mitigation, and cost optimization. Learn to map unfamiliar problem statements to these patterns, identify architectural components, and discuss trade-offs to structure your design approach effectively.
A candidate gets a problem statement like "design a multilingual compliance assistant for a global bank," hears words like multilingual, compliance, and banking, and freezes on the domain. The stall usually has little to do with architecture skill. It happens because the surface language looks new while the underlying system shape is familiar.
Strong interview performance starts when you compress novelty into repeatable patterns. You already have SCALED as a way to turn requirements into a design. What you need now is a recognition layer that tells you which building blocks to activate first.
This lesson gives you that fast filter. You will map unfamiliar problem statements onto four recurring patterns: RAG orchestration, feedback loops, hallucination mitigation, and cost optimization. The goal is not to memorize canned answers. It is to decompose interview questions quickly into architecture choices, safety controls, and trade-offs, then move into a structured design discussion.
What are the four recurring patterns
Treat these patterns as a compact recognition toolkit for GenAI System Design interviews. An interview question often sounds domain-specific, but the request path usually reduces to one or more recurring mechanisms in the system.
The patterns usually appear in the following forms.
RAG orchestration: The system must fetch external knowledge at request time when answers depend on private data, changing documents, or source-aware output.
Feedback loops: The system must collect signals from usage, convert them into labels or metrics, and use them to improve prompts, retrieval, routing, or models over time. ...