Your AI Interview Prep Roadmap
A structured learning path for AI engineer interviews — not a random collection of articles. Each module teaches the concepts interviewers actually test, then links to practice questions and spoken mock interviews.
Not sure where to start?
Pick the path that matches your background and interview goals.
Software Engineer Adding AI Skills
You know engineering. Here's the AI layer.
- Prompt Engineering: Patterns That Actually Work
- RAG: From Basics to Production Systems
- LLM Evaluation: Metrics, Evals, and Production Monitoring
- AI System Design: How to Approach Any AI Architecture Question
- AI Agents & Tool Use: Design Patterns for Autonomous Systems
- How to Practice System Design Out Loud
AI/ML Engineer Preparing for Senior Roles
You know the concepts. Now practice explaining them.
- AI System Design: How to Approach Any AI Architecture Question
- RAG: From Basics to Production Systems
- LLM Evaluation: Metrics, Evals, and Production Monitoring
- AI Agents & Tool Use: Design Patterns for Autonomous Systems
- Prompt Engineering: Patterns That Actually Work
- How to Practice System Design Out Loud
Targeting AI-Native Companies
These companies go deep. Here's what they test.
- AI System Design: How to Approach Any AI Architecture Question
- RAG: From Basics to Production Systems
- LLM Evaluation: Metrics, Evals, and Production Monitoring
- AI Agents & Tool Use: Design Patterns for Autonomous Systems
- Prompt Engineering: Patterns That Actually Work
- How to Practice System Design Out Loud
Topic Modules
Deep educational content on each topic — with interview framing, key concepts, and links to practice questions.
RAG: From Basics to Production Systems
Learn how Retrieval-Augmented Generation works, why every AI company uses it, and how to explain RAG architecture in an interview.
LLM Evaluation: Metrics, Evals, and Production Monitoring
Understand how to evaluate LLM outputs, build eval suites, and monitor production AI systems — critical knowledge for AI engineering interviews.
AI System Design: How to Approach Any AI Architecture Question
A structured framework for AI system design interviews — from requirements to architecture to tradeoffs.
Prompt Engineering: Patterns That Actually Work
Learn the prompt engineering patterns that matter in interviews — from chain-of-thought to structured output to few-shot learning.
AI Agents & Tool Use: Design Patterns for Autonomous Systems
Learn the core design patterns for AI agents — ReAct, plan-and-execute, multi-agent systems, and tool use — and how to discuss them in interviews.
How to Practice System Design Out Loud
The gap between knowing AI concepts and explaining them in a live interview is massive. This guide teaches you how to practice the spoken part.
Ready to practice?
The learning guide teaches concepts. Now prove it with a real AI mock interview — 3 free sessions, no credit card needed.
Frequently Asked Questions
How do I prepare for an AI engineer interview?▾
Start by understanding the core topics: RAG, LLM evaluation, AI system design, prompt engineering, and AI agents. Study each topic with interview framing — not just what the concepts are, but how to explain them under pressure. Then practice out loud with mock interviews. Reading alone isn't enough — spoken practice is what separates prepared candidates from the rest.
What topics are tested in AI engineering interviews?▾
Most AI engineering interviews cover five main areas: RAG and retrieval systems, LLM evaluation and production monitoring, AI system design (end-to-end architecture), prompt engineering techniques, and AI agents with tool use. The emphasis varies by company — AI-native companies go deeper, while companies adding AI features focus more on system design and RAG.
How is an AI interview different from a regular software engineering interview?▾
AI interviews add probabilistic systems to the mix. Instead of deterministic code that either works or doesn't, you're designing systems with uncertain outputs. This means evaluation is harder, failure modes are different (hallucination, quality degradation), and cost structures are unique (per-token pricing). You also need to reason about tradeoffs specific to AI: latency vs quality, RAG vs fine-tuning, single model vs ensemble.
How do I practice system design interviews out loud?▾
Use a timer, pick a design question, and talk through your solution as if an interviewer were listening. Structure your answer: 5 minutes on requirements, 5 minutes on high-level architecture, 15 minutes on a deep dive, and 5 minutes on tradeoffs. Record yourself or use a mock interview tool like Rubduck that provides real-time pushback and transcript-linked feedback.
Prep for the full interview loop
Know the concepts. Now prove it. Practice GenAI, Coding, System Design, and AI/ML Design interviews with an AI that tells you exactly where you fell short.
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