Master AI system design — and the interviews that test it
Engineering interviews and real production work now expect you to design AI systems, not just CRUD apps. This masterclass closes that gap with the same rigor used inside FAANG architecture reviews, applied to real LLM, RAG, agentic, and multimodal systems.
You'll first build the foundations that every AI system rests on: the latency–throughput–cost triangle, LLM inference internals (tokens, context windows, KV cache), embeddings and ANN vector search, RAG architecture, caching strategies, guardrails and evaluation loops, streaming and backpressure, and cost optimization through routing, distillation, and quantization.
Then you apply them across 11 production case studies, including:
- A ChatGPT-style conversational assistant
- A production RAG system over 10M documents
- A multimodal (text + image) search engine
- A code assistant like GitHub Copilot
- A multi-agent research workflow and a real-time AI voice agent
- An LLM observability and evaluation platform
- A compliance-safe healthcare assistant and an enterprise knowledge base with access control
You'll also cover reliability, safety, and compliance — hallucination mitigation, red-teaming, PII and privacy-preserving inference, SOC 2 / GDPR / HIPAA, and graceful degradation — then finish with reference architectures and a FAANG interview playbook.
Who this is for: engineers preparing for AI-focused system design interviews, or building LLM systems at scale and want a proven mental model. By the end you'll be able to whiteboard and defend a production AI architecture with confidence.
