AI Engineering skill assessment
Build with LLMs — RAG, agents, evals, and AI systems.
What it covers
LLM Fundamentals & Prompting
Every other skill in this track assumes fluency here: how models tokenize input, why a bigger context window isn't free, and why prompt structure changes output reliability more than model choice does.
Retrieval-Augmented Generation
RAG is how production LLM apps stay accurate and current without retraining — know how to fetch the right passages, ground answers in them, and prove the grounding actually held.
Agents & Tool Use
Tool-calling loops turn an LLM from a text generator into something that can act — this single-agent mechanic underlies nearly every agent you'll build, debug, or get asked about in an interview.
Multi-Agent Systems
Coordinating several agents adds real cost, latency, and new failure modes, so know the orchestration patterns that pay off — and when one well-scoped agent beats a fleet of them.
Evaluation & Guardrails
Ship AI features on evidence, not vibes — evals catch quality regressions before users do, and guardrails contain the failure modes (injection, jailbreaks, unsafe output) that evals alone can't prevent.
AI System Design
Most production AI failures are architecture failures, not model failures — decide when a fixed workflow beats an autonomous agent, and how to manage context and state as a system scales past a single call.
Cost, Latency & Safety
An AI feature that's accurate but too slow or expensive doesn't ship, and one that's fast and cheap but unsafe doesn't survive review — know the levers for all three before you're asked to fix them under pressure.
Ready to benchmark your AI Engineering skills?
221 questions · about 5 minutes · see your level and percentile instantly.