The AI-enabled track
AI Coding & AI Engineering Interviews
Interviews changed. Companies now test how you build with AI — and how you design the systems AI runs on. This track covers both: the engineering judgment for AI-assisted coding rounds, and the system design depth for AI infrastructure roles.
AI System Design Case Studies
The five AI infrastructure designs interviewers ask in 2026 — each with walkthroughs, interviewer scorecards, pressure questions, and free guided practice.
LLM Inference Serving
Continuous batching, KV-cache economics, and the TTFT vs throughput trade.
Case study + guided practice →
RAG Pipeline
Chunking, hybrid retrieval, permission-aware search, and retrieval evals.
Case study + guided practice →
AI Agent Orchestration
Durable runs, idempotent tools, budget governors, approval gates.
Case study + guided practice →
Foundation Model Platform
Training at 10K GPUs: checkpointing math, data pipelines, topology.
Case study + guided practice →
AI Code Assistant
The 200ms completion budget: context engines, prefix caching, cancellation.
Case study + guided practice →
All 21 case studies →Coding with AI — what interviewers actually grade
More companies run AI-enabled coding rounds where assistants are allowed. The bar didn't drop — it moved.
Drive the tool, don't be driven
Interviewers watching you code with AI grade one thing above all: who is in charge. Strong candidates decompose the problem first, then direct the assistant at well-scoped pieces — they never paste the whole prompt and pray.
Verify like a reviewer
Every AI-generated block gets the same treatment you'd give a junior's PR: trace the edge cases, question the complexity, run the tests. Accepting wrong code confidently is the fastest fail in an AI-enabled interview.
Know what the machine can't
Requirements, invariants, trade-offs, and system boundaries stay human. The interview signal has shifted from 'can you write a loop' to 'can you specify, verify, and integrate' — which is exactly what our system design track trains.
Narrate your loop
Prompt → inspect → test → refine, out loud. The meta-skill interviewers reward is a tight, articulated iteration loop — the same loop that makes you fast with agents at work.
AI Engineering Guides
Tutorials and interview prep for the agentic stack.
MCP vs A2A: The Two Protocols in Every 2026 Agent Stack, Explained
9 min read · AI Engineering · AI Agents · MCP
AI Agent Memory Architectures: Short-Term, Long-Term, and Checkpointing — What Interviews Actually Test
10 min read · AI Engineering · AI Agents · LangGraph
Google's Agent2Agent (A2A) Protocol, Explained: How AI Agents Talk to Each Other
8 min read · AI Engineering · AI Agents · Agent2Agent
LangGraph Tutorial: Building Stateful, Multi-Step AI Agent Workflows (Free)
9 min read · AI Engineering · LangGraph · Tutorial
LangChain Tutorial: Building Your First AI Agent (Free, Step-by-Step)
9 min read · AI Engineering · LangChain · Tutorial
5 Follow-Up Questions AI System Design Interviewers Always Ask (And How to Answer Them)
7 min read · AI Engineering · System Design · Interview Prep
AI Engineer System Design Interviews in 2026: The Complete Prep Roadmap
8 min read · AI Engineering · System Design · Interview Prep
Test yourself under real conditions
Free guided practice on every AI case study — then take a realistic AI mock interview when you're ready to know where you stand.