Overview
The AI system design interview, answered. Ten volumes cover the foundations of AI system design, LLM-based systems, RAG system design, agent system design, production scaling, and real-world design scenarios, then go deeper into LLM inference and serving infrastructure, AI platforms and lifecycle systems, modern agent and multimodal systems, and classic interview prompts with estimation drills and architecture critiques. Every answer names the trade-off and the constraint behind it, and many carry an architecture diagram.
What you will learn
- Structure an AI system design interview answer from requirements to trade-offs
- Design LLM, RAG and agent systems for latency, cost and reliability
- Scale AI systems in production and explain where they fail
- Design inference and serving infrastructure, including caching and batching
- Reason about AI platforms, model lifecycle and evaluation systems
- Do back-of-envelope estimation for tokens, throughput and cost
- Critique an AI architecture and say what you would change
Included with the kit
- 25 written parts, yours for good
- 13h 1m of reading, measured not estimated
- Written for the advanced level
- Every future revision included
- UPI, cards and netbanking
Prerequisites
- Backend or ML engineering experience
- Familiarity with LLMs and RAG at a working level
Curriculum
10 sections · 25 parts · 13h 1m01Volume 1: Foundations of AI System Design1 part · 67 min
Build system design thinking specifically for AI
- Foundations67 min
02Volume 2: Designing LLM-Based Systems1 part · 79 min
Deep dive into LLM-first architectures
- Designing LLM-Based Systems79 min
03Volume 3: RAG System Design (Core)1 part · 78 min
Design retrieval-based systems end-to-end
- RAG System Design78 min
04Volume 4: Agent System Design1 part · 79 min
Design agentic systems (single + multi-agent)
- Agent System Design79 min
05Volume 5: Production AI Systems (Scaling)1 part · 67 min
Make systems production-ready
- Production AI Systems (Scaling)67 min
06Volume 6: Real-World System Design Scenarios1 part · 64 min
Apply everything in interview-style scenarios
- Real-World System Design Scenarios64 min
07Volume 7: LLM Inference & Serving Infrastructure5 parts · 85 min
Design, size, and run self-hosted LLM inference on GPUs, from batching and KV cache behavior to capacity math, optimization, autoscaling, and the self-host vs API decision.
- Inference Platform Design19 min
- Batching, Scheduling & KV Cache19 min
- GPU Capacity Planning & Estimation19 min
- Optimization: Quantization, Speculative Decoding & Multi-LoRA12 min
- Autoscaling, Admission Control & Self-Host vs API16 min
08Volume 8: AI Platforms & Lifecycle Systems5 parts · 92 min
Design the shared platforms that sit behind every AI product: the LLM gateway, the fine-tuning and model lifecycle stack, evaluation and labeling services, large batch jobs, and the vector and data infrastructure underneath them.
- LLM Gateway & Internal AI Platform21 min
- Fine-Tuning & Model Lifecycle Platform18 min
- Evaluation & Labeling Platforms18 min
- Batch LLM Processing at Scale16 min
- Data & Vector Infrastructure19 min
09Volume 9: Modern Agent & Multimodal Systems5 parts · 96 min
Design the agent, safety, multimodal, and edge systems that 2026 AI products are built on, from MCP tool platforms and computer-use agents to real-time voice and on-device models.
- Tooling & Protocols: MCP and A2A20 min
- Named Agent Designs21 min
- Guardrails & Safety Services16 min
- Multimodal Systems22 min
- Edge & Hybrid AI17 min
10Volume 10: Classic Prompts, Estimation & Architecture Critiques4 parts · 74 min
Practice the full interview loop: the classic 2026 design prompts, back-of-envelope math with real numbers, critiquing a flawed architecture, and adapting one design through a chain of interviewer follow-ups.
- Classic Design Prompts21 min
- Back-of-Envelope Estimation Drills16 min
- Architecture Critiques21 min
- Follow-Up Chains16 min
Reviews
to review this kit once you have finished it.