INTERVIEW PREP · GENAI · LLM · AI/ML ENGINEERS

957+ practice questions from actual AI & ML interview loops, with answers that get offers.

Every question ordered easy → hard into a journey per topic. Written by engineers who sat the loops at the labs hiring right now.

Start practicing free →no card · free answers in every topic

QUESTION OF THE DAYDesign a multi-agent customer support system with escalation to humans.System Design for AI in Production · hardAnswer →

QUESTIONS MIRROR REAL LOOPS AT

The content is the product.

A real, unlocked card from the RAG & Agents journey:

Customer's RAG pipeline started hallucinating after a data refresh — debug it live

mediumOpenAI

Resist the urge to touch the prompt. A data refresh changes retrieval, not generation — so measure retrieval before you theorize.

1 · REPRODUCE AND MEASURE

# golden set recall, before touching anything
hits = retriever.search(q, k=20)
print(recall_at_k(hits, gold))  # 0.31 — was 0.86

Recall cratered. The refresh re-ran ingestion but skipped re-embedding, so the index points at stale vectors.

THE LEARNING ENGINE

Not a wall of answers. A system that learns how you learn.

Reading answers from an LLM is passive, and it forgets you the moment you close the tab. This is built on knowledge tracing, the same learning science behind the best intelligent tutoring systems. It quietly models how well you recall each topic and points you to exactly what to study next.

▸Every concept closes with a self-check drill, so you practice active recall instead of just reading.
▸One tap after each answer (Got it / Shaky / Review) feeds a live mastery estimate per topic.
▸“Your next focus” sends you to your weakest area, not a random question.
▸Your mastery and journey persist, so every session builds on the last.

MASTERY BY TOPICexample

System DesignStrong 78%

RAG & AgentsStrong 61%

LLM & GenAIDeveloping 44%

MLOpsBuilding 22%

NEXT FOCUSImplement an idempotent agent that survives a mid-run crash→

11 TOPICS · CURATED JOURNEYS

LLM & GenAI Fundamentals135

start

RAG & Agent System Design104

start

Coding & DSA144