Role guide · Free PDF
AI Engineer
LLM fundamentals, RAG, agents, evals and the questions about your own projects.
Interview prep for AI jobs
Interview Guides for Every AI Role
AI engineer, ML engineer, data scientist, MLOps and AI product manager, plus system design for agents, RAG, ML systems and databases. The questions you’ll get, the answers to give, and the follow-up that comes next.
New to AI? Start here
AI Basics: What Everyone Should Know
The AI ideas everyone should know, in plain language: how ChatGPT works, why it makes things up, and what AI can and can’t do. No math, no jargon, and a one-sentence version of every idea you can repeat in a meeting or an interview.
Guides by role
Prep for the Job You Want
Each role gets its own interview loop. Each guide follows it, round by round.
Role guide · Free PDF
ML Engineer
ML fundamentals, model training and evaluation, feature pipelines and the ML system design round.
Role guide · Free PDF
Generative AI Engineer
How LLMs work, prompting, fine-tuning vs RAG, structured outputs, cost and latency.
Role guide · Free PDF
MLOps Engineer
Deploying and serving models, pipelines, monitoring, drift and rollbacks.
Role guide · Free PDF
Data Scientist
Statistics, experiments, SQL, modeling and the product case questions.
Role guide · Free PDF
AI Product Manager
When to use AI, how to measure it, risk and trust, and the product sense round.
System design
One Guide per Kind of System
The design round is open-ended and hard to practice alone. Work through the systems you’re likely to be asked about.
System design · Free PDF
AI System Design
A framework for the design round and worked cases: support bot, document search, coding agent and more.
System design · Free PDF
System Design for AI Agents
Agent loops, tools, state and memory, permissions, and how agents fail in production.
System design · Free PDF
System Design for RAG and LLM Apps
Ingestion, chunking, retrieval, reranking, grounding and evaluating answers.
System design · Free PDF
System Design for ML Systems
Recommendations, ranking, search and fraud detection, from data to serving.
System design · Free PDF
System Design for LLM Inference
Serving models at scale: batching, caching, GPUs, streaming and cost per request.
System design · Free PDF
System Design for Databases
Choosing a database, indexing, replication, sharding, consistency and vector stores.
System design · Free PDF
System Design for Data Pipelines
Batch and streaming, queues, backfills, data quality and feature stores.
Try a Question from the Guides
Answer it out loud first, then open the model answer.
System design for RAG and LLM apps
When would you not use RAG?
Show the model answer ⌄ Hide the answer ⌃
When the knowledge isn't unstructured text that people search by meaning. If the corpus is small and stable, I paste it into the prompt: no retrieval step means no retrieval misses. If the question is structured, like "orders over $500 last month," that's a SQL query behind a tool, because embeddings are fuzzy and the question is exact. If the data changes per request, like an account balance or the user's current plan, I call the live API instead of indexing a snapshot that goes stale. And if it's general, stable knowledge the model already handles well, retrieval only adds latency.
My two sorting questions are: is it structured, and does it change per request? A yes to either usually means a tool, not an index. RAG's lane is semi-stable documents people search by meaning, like a handbook or a help center.
- What they’re checking:
- Whether you treat RAG as one option among several and route knowledge by its shape, instead of reaching for a vector database by default.
- Likely follow-up:
- A user asks "what plan am I on?" How would you answer it, and why not from the index?
What’s Inside Every Guide
Built for the weeks before the interview, not a semester of reading.
The questions for your role
Grouped the way the interview loop runs: fundamentals, building, production, your projects.
Answers you can say
About a minute each: the answer first, one example, one tradeoff.
What they’re checking
The one thing each question tests, so you know what a strong answer has to show.
The follow-up
The question that comes next, so it doesn’t catch you off guard.
The mistake to avoid
The weak answer interviewers hear most, and why it costs you.
A PDF to keep
Print it or read it on your phone on the way to the interview.
FAQs
Frequently Asked Questions
What is AI Explorers Club?
Interview prep for AI jobs: one guide per role (AI engineer, ML engineer, generative AI engineer, MLOps, data scientist, AI product manager), a system design guide for each kind of system you may be asked to design, and AI Basics for anyone new to AI.
What is in a guide?
The questions that come up for that role, each with a model answer you can say in about a minute, what the interviewer is checking, the follow-up they will ask, and the mistake to avoid. As a PDF you can print or read on your phone.
Are the guides free?
The PDFs are free: leave your email and we send each one when it is ready. 155 questions are already on the site, with sample answers.
How is this different from asking ChatGPT?
A chatbot answers whatever you ask. A guide decides what to study: the questions for your role, answers sized for a spoken reply, and the follow-up you will get next.
From the Blog
All posts →- Interview Prep The AI Engineer Interview Process: Rounds and What Each Tests The rounds you're likely to meet in an AI engineer interview loop, what each one is checking, and how to prepare for it.
- Careers AI Engineer vs ML Engineer: The Job and the Interview An AI engineer builds products on top of foundation models; an ML engineer trains and serves models. How the daily work, the skills and the interview loops differ.
- Careers How to Become an AI Engineer: A Practical Roadmap An AI engineer roadmap for software engineers and new grads: the skills to learn in order, four portfolio projects and what each proves, and how to get through the interview.
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