Course
AI System Design Course, explained simply.
Follow this course when you want a structured path through AI system design. Each module is written as a practical lesson with diagrams, checklists, and prompts you can use for interviews, teaching, or real product design.
How to follow
Use the same routine for every module.
Ready modules
Start with these first
These modules are already rewritten as local lessons with diagrams, checklists, and practice prompts.
Interview Prep and Answer Frameworks
A practical way to answer AI system design questions without jumping straight into architecture. The source guide uses structured frameworks such as SPIDER for system design answers. This note turns that into a simple interview routine you can rehearse.
Open module →Module 2RAG Fundamentals
RAG is not only retrieval plus generation. A useful RAG system is a trust pipeline that decides what knowledge is allowed, what evidence is relevant, how fresh it is, whether the user can see it, and how the answer should admit uncertainty.
Open module →Module 3Agentic Systems
Agentic systems move beyond chat by combining reasoning, actions, memory, and feedback loops. The source chapter frames modern agents as closed-loop reasoning systems rather than simple prompt wrappers.
Open module →Course map
Full learning path
Ready modules can be opened now. Upcoming modules show the complete direction of the course so learners understand the roadmap.
Interview Prep and Answer Frameworks
A practical way to answer AI system design questions without jumping straight into architecture. The source guide uses structured frameworks such as SPIDER for system design answers. This note turns that into a simple interview routine you can rehearse.
Open module →RAG Fundamentals
RAG is not only retrieval plus generation. A useful RAG system is a trust pipeline that decides what knowledge is allowed, what evidence is relevant, how fresh it is, whether the user can see it, and how the answer should admit uncertainty.
Open module →Agentic Systems
Agentic systems move beyond chat by combining reasoning, actions, memory, and feedback loops. The source chapter frames modern agents as closed-loop reasoning systems rather than simple prompt wrappers.
Open module →Foundations
Transformers, attention, embeddings, tokens, and the mental model behind LLM behavior.
Planned moduleModel Landscape
How to compare and choose models for production constraints.
Planned moduleTraining and Adaptation
Fine-tuning, LoRA, preference tuning, distillation, and when not to train.
Planned moduleInference Optimization
Serving, caching, batching, latency, throughput, and cost optimization.
Planned modulePrompting and Context
Prompt structure, context selection, examples, and prompt safety.
Planned moduleMemory and State
Conversation memory, user preference, task state, and audit trails.
Planned moduleFrameworks and Tools
LangGraph, LlamaIndex, DSPy, coding tools, and orchestration frameworks.
Planned moduleDocument Processing
OCR, layout parsing, extraction confidence, review UI, and correction loops.
Planned moduleInfrastructure and MLOps
Deployment, monitoring, cost, rollback, model routing, and platform operations.
Planned moduleSecurity and Access
Permissions, isolation, tenant boundaries, secrets, and audit controls.
Planned moduleReliability and Safety
Guardrails, fallback paths, red-teaming, and safe release patterns.
Planned moduleEvaluation and Observability
Golden tests, traces, feedback, benchmarks, drift, and production monitoring.
Planned moduleAI Design Patterns
Reusable patterns and anti-patterns for AI system design.
Planned moduleCase Studies
Real-world architecture problems and how to reason through them.
Planned moduleTool Use and Computer Agents
Computer-use systems, tool-use patterns, governance, and safety boundaries.
Planned module