Learning Path
System Design for AI/FDE
Distributed systems and AI infrastructure design for FDE-style interviews and production architecture decisions.
System Design Foundations for AI Builders
Learn the vocabulary behind scalable products before applying it to AI systems.
Storage, APIs, and Auth Basics
Understand the storage and API decisions that shape reliable AI applications.
Reliability Basics for AI Products
Use SLIs, SLOs, health checks, observability, circuit breakers, and autoscaling to keep user trust.
FDE System Design Starter Scenarios
Practice explaining AI-adjacent systems to technical and non-technical stakeholders.
Scaling Patterns: Hashing, Sharding, and Replication
Design data distribution and replication strategies with explicit trade-offs.
Service Communication and Mesh Patterns
Choose between synchronous APIs, async queues, service discovery, and service mesh.
Database Internals and Storage Tiers
Reason about indexes, isolation, Redis, Bloom filters, and hot/cold data.
Reliability and Interview Walkthroughs
Apply tracing, chaos engineering, error budgets, canaries, and full design walkthroughs.
LLM Inference and Serving Architecture
Design high-throughput model serving with batching, KV cache, routing, and cost controls.
Production RAG, Vector Search, and Embeddings
Design retrieval systems that balance recall, latency, grounding, and freshness.
Multi-Agent, MCP, and Prompt Caching Systems
Design AI-native control planes with agent orchestration, tool protocols, and cache efficiency.
Safety, Compliance, and Human Approval Pipelines
Layer safety, auditability, and human review into AI infrastructure from the start.
Global Distributed Systems for AI Infrastructure
Handle multi-region design, consensus, failure modes, advanced caching, and streaming data.