🧠 AI System Design Interview Prep

June 29, 2026 · View on GitHub

Design production LLM systems under interview pressure — gateways, caching, streaming, cost, evals, and the trade-offs senior interviewers actually probe. Not flashcards: real decision matrices, architecture diagrams, and "what the interviewer asks next."

⭐ If this helps your prep, star it — it’s the fastest way to help other engineers find it.

If you're preparing for an AI / ML system design interview — LLM system design, GenAI infrastructure, or an AI platform / ML infra role — this repo gives you the senior-level answers: how to choose models, design LLM gateways, cache and route requests, control cost, stream responses, and build evals the way production teams actually do. Every answer leads with the decision, backs it with a trade-off table or diagram, and ends with the follow-up the interviewer asks next.

📚 AI Interview Prep series

This repo is the system-design hub. Topic depth lives in dedicated companion repos — each focused so you can go deep without wading through everything else:

Repo	Focus	Go here for
AI System Design Interview (you are here)	LLM fundamentals + end-to-end system design	Model selection, gateways, caching, streaming, cost, evals, full design walkthroughs
🔎 rag-interview-system	Retrieval-Augmented Generation, in depth	200+ RAG Q&As: chunking, vector DBs, hybrid search, rerankers, RAG evaluation
🤖 ai-agents-design-patterns	Agentic systems & patterns	ReAct/Plan-Execute, tool use, multi-agent orchestration, memory, guardrails

Preparing for a full AI system design round? Work through this repo, then go deep on RAG and Agents in the companion repos. ⭐ all three.

👋 Who This Is For

Software engineers preparing for Senior / Staff / Principal AI roles
Backend engineers moving into ML platform or AI infrastructure positions
Candidates interviewing at FAANG, AI startups, FinTech, InsurTech, and SaaS companies
Engineers who want to understand production AI systems, not just theory

🗂️ What's Inside

Module	Topics	Questions
LLM Fundamentals	Model selection, fine-tuning, prompting, serving, evals, context	20
System Design Patterns	Gateways, routing, caching, streaming, cost, scale, observability	25
Case Studies	Full worked design walkthroughs (LLM gateway, code assistant…)	3
Resources	Study plan, cheat sheets, mock interview template	—
🔎 RAG	→ rag-interview-system	200+
🤖 AI Agents	→ ai-agents-design-patterns	—