GenAI System Design Interview Preparation

GenAI is no longer a novelty; it’s infrastructure. From code completion and search to customer support and data analysis, GenAI is reshaping how modern systems are built and how high-stakes product decisions are made. As a result, understanding GenAI is no longer optional for engineers. It’s now a core concern in System Design interviews at top tech companies.

Earlier in this course, we examined what generative AI systems are. Now, we shift to what matters most in interviews: reasoning about them clearly under pressure.

The interview moment where things change

Imagine you’re in a System Design interview. You’ve drawn a clean architecture. The model choice makes sense. The data flow looks solid. Then the interviewer leans in and asks:

• How do you reduce hallucinations without blowing up latency?

• What happens if model costs triple overnight?

• Why does this system need a dynamic contextualizer, and what fails without it?

Suddenly, the problem isn’t about knowing GenAI concepts anymore. This is where most candidates stumble. Not because they don’t understand GenAI, but because they don’t understand how interviewers evaluate System Design thinking for AI-powered systems.

Let’s start with a clear description of what a GenAI System Design interview is and how to navigate it.

What is a GenAI System Design interview?

A GenAI System Design interview evaluates a candidate’s ability to design end-to-end systems that use large language models effectively and at scale.

Unlike traditional machine learning interviews, the focus is not on:

• Model architectures

• Training algorithms

• Loss functions or fine-tuning tricks

Instead, interviewers care about whether a candidate can:

• Integrate GenAI components such as LLMs, embeddings, retrieval systems, and orchestration layers

• Build systems that are scalable, reliable, and cost-aware

• Make clear trade-offs when constraints change unexpectedly

In other words, this is still a System Design interview, but the systems are probabilistic, expensive, and non-deterministic, which fundamentally changes how you reason about them.

Interviewer’s expectations in the interview

Despite the new components, the interviewer’s evaluation framework is familiar. FAANG interviewers expect candidates to apply the same structured thinking used throughout this course:

1. Start with requirements and constraints: Clearly identify the requirements, scale expectations, latency targets, cost limits, and quality requirements before proposing any architecture.

2. Select the GenAI model: Choose an appropriate GenAI model based on task complexity, accuracy needs, latency tolerance, and cost, and justify why it fits the use case.

3. Discuss the training infrastructure: Explain how data collection, training pipelines, compute resources, and evaluation workflows would be designed to support model development and iteration.

4. Propose the inference pipeline: Describe how requests flow through prompt construction, retrieval or conditioning, model inference, and post-processing in a scalable and reliable manner.

5. Incrementally evolve the design: Refine the system by addressing bottlenecks and constraints, clearly explaining trade-offs between latency, cost, scalability, reliability, and output quality.

What changes is where the complexity lives.

You’re expected to demonstrate:

• An understanding of training vs. inference workflows

• The ability to estimate latency, throughput, and cost for each workflow

• Awareness of model variability, hallucinations, and failure modes

• A clear understanding of how GenAI systems differ from deterministic services

How GenAI System Design interviews are scored

Most candidates assume interviewers are grading them on correctness. They aren’t.

Interviewers are evaluating:

• Clarity of thinking

• Trade-off awareness

• Ability to adapt when constraints change

• How well you communicate complex systems under time pressure

Two candidates might draw similar diagrams, but the one who clearly explains why the system is designed that way will score significantly higher.

How to prepare for GenAI System Design interviews using this course

This course is designed to help you build the mindset needed to succeed in a GenAI system design interview.

To prepare effectively, focus on mastering the core concepts and patterns that recur frequently in real-world GenAI systems. You should develop a strong understanding of:

• Text-to-text systems, such as conversational assistants and search

• Multimodal systems like text-to-image, text-to-audio, text-to-video generation, and image captioning systems

Your preparation should emphasize end-to-end System Design, not isolated components. That means being able to reason clearly about:

• Input processing and request validation

• Embeddings, vectorization, and similarity search

• Model training and evaluation infrastructure

• Prompt construction and dynamic contextualization

• Model inference and scaling strategies

• Output handling, guardrails, and post-processing

Just as importantly, you should be able to explain why each component exists and how design choices impact latency, cost, scalability, reliability, and output quality. Reinforce this understanding by reviewing mock interviews, studying design walkthroughs, and practicing your explanations out loud, just as you would in a real interview.

Typical GenAI System Design interview questions

GenAI System Design interviews typically ask candidates to architect scalable and reliable GenAI systems while justifying their design choices under changing constraints. The problems are intentionally open-ended. Interviewers want to see how candidates reason about trade-offs involving latency, cost, data handling, and model behavior, especially when assumptions break.

Common questions include:

• How would you design a conversational AI system (ChatGPT-like) at scale?

• What trade-offs exist between latency, cost, and model quality, and how would you navigate them?

• How would you monitor, evaluate, and debug a GenAI system in production?

• How do multimodal generative systems work (text-to-image, text-to-audio, text-to-video)?

Strong candidates don’t just propose architectures. They explain why choices were made, how the system behaves at scale, and what they would change if constraints around cost, traffic, or quality suddenly shift.

That’s the level of thinking this course is designed to help you reach.

Common mistakes and pitfalls

Most candidates don’t fail GenAI System Design interviews because their designs are “wrong.” They fail because their thinking is hard to follow.

Common mistakes include:

• Overengineering early, before clarifying requirements

• Diving into model internals or ML theory

• Skipping trade-offs between latency, cost, quality, and reliability

• Treating GenAI components as magic boxes instead of services with constraints

Strong candidates do the opposite. They keep designs interview-ready, explain decisions clearly, and treat the LLM as one part of a larger, evolving system. They prioritize clarity, structure, and reasoning over completeness.

Conclusion

GenAI System Design interviews are about demonstrating sound System Design judgment under uncertainty. Interviewers want to see how you reason about scale, cost, latency, and failure in systems powered by probabilistic models. If you can approach GenAI systems with structure, articulate clear trade-offs, and adapt your design when constraints change, you’re already operating at the level these interviews expect.

In the next lesson, we’ll walk through a practical framework for designing any GenAI system, one that keeps your thinking structured, focused, and interview-ready under pressure.