AI System Design
Design production-ready AI systems. From RAG pipelines to recommendation engines, learn architecture patterns used at top tech companies.
All Courses
20 comprehensive courses covering every aspect of AI system design.
Fundamentals & Core
AI System Design Fundamentals
Core principles, trade-offs, and architectural thinking for AI systems.
8 LessonsDesigning LLM Applications
Build production LLM apps with prompt management, guardrails, and evaluation.
8 LessonsDesigning RAG Systems
Retrieval-augmented generation pipelines with chunking, indexing, and reranking.
8 LessonsDesigning Multi-Agent Systems
Orchestrate multiple AI agents with communication, planning, and coordination.
7 LessonsInfrastructure & Serving
Designing ML Inference
Low-latency model serving with batching, quantization, and hardware optimization.
7 LessonsDesigning ML Training Pipelines
Scalable training infrastructure with distributed computing and experiment tracking.
7 LessonsDesigning ML Feature Stores
Centralized feature management for online and offline ML workloads.
7 LessonsDesigning AI Data Pipelines
ETL, streaming, and batch processing for AI-ready data at scale.
7 LessonsApplications
Designing AI Chatbot Platforms
Conversational AI with intent routing, context management, and multi-turn dialogue.
7 LessonsDesigning Recommendation Systems
Collaborative filtering, content-based, and hybrid recommendation architectures.
8 LessonsDesigning AI Search Engines
Semantic search, vector retrieval, and AI-powered ranking systems.
7 LessonsDesigning Voice AI Systems
Speech-to-text, text-to-speech, and real-time voice processing pipelines.
7 LessonsDesigning Computer Vision Pipelines
Image classification, object detection, and video processing at scale.
7 LessonsDesigning AI Content Moderation
Automated moderation with text, image, and video classification systems.
7 LessonsPlatform & Operations
Designing AI Gateway
API gateway patterns for routing, rate limiting, and multi-provider orchestration.
7 LessonsDesigning AI Monitoring
Observability, drift detection, and alerting for production AI systems.
7 LessonsDesigning AI Cost Optimization
Reduce inference costs with caching, tiering, and resource management strategies.
7 LessonsDesigning AI Security
Prompt injection defense, data privacy, and secure AI deployment patterns.
7 LessonsDesigning AI for Edge
On-device inference, model compression, and edge-cloud hybrid architectures.
7 LessonsInterview Prep
What You'll Learn
Skills you will gain across these 20 courses.
Architecture Patterns
Master proven design patterns for RAG, LLM apps, multi-agent systems, and real-time AI pipelines used at scale.
Production Code
Go beyond prototypes. Learn to build reliable, maintainable AI systems with proper monitoring and error handling.
Cost Optimization
Reduce AI infrastructure costs with smart caching, model tiering, batching, and resource management strategies.
Interview Ready
Prepare for AI system design interviews with structured frameworks, real questions, and end-to-end design walkthroughs.
System Design for AI is different from classical system design in a few ways that matter. The expensive components are different (GPU-hours, context-window tokens, embedding storage), the latency profiles are different (streaming, batching, KV cache reuse), the failure modes are different (hallucination, refusal, drift), and the reviewers of your design are different (a product manager and a risk officer are now equally likely to ask hard questions). This track is system design re-grounded for those realities.
We cover the canonical AI system design interviews (design a RAG system, design a recommendation engine, design an LLM chatbot at scale, design a real-time fraud detector, design a multi-agent system, design an ML inference platform) in enough depth that you can explain and defend a design to a senior engineer. Each lesson emphasizes the decision points where AI system design diverges from its classical analogue, because that is almost always where a candidate wins or loses the interview, and where a real system succeeds or fails.