Question 1
Difficulty: hard
How would you design a retrieval-augmented generation system for a customer support assistant that needs accurate, up-to-date answers?
Sample answer
I’d start by being clear on the failure modes: outdated answers, hallucinations, and irrelevant retrieval. My first step would be to define the knowledge sources and the freshness requirements, then build an ingestion pipeline that chunks documents in a way that preserves meaning, not just token count. I’d use embeddings plus metadata filters so retrieval can narrow by product, version, region, and date. For the generation layer, I’d keep the prompt focused on cited context and explicitly instruct the model to say when it doesn’t know. I’d also add a reranking step for better relevance and measure answer quality with both offline test sets and live user feedback. In practice, I’d monitor retrieval hit rate, groundedness, latency, and escalation rate. A system like this works best when it’s treated as a product loop, not a one-time model integration.
Question 2
Difficulty: medium
Tell me about a time you had to improve the quality of AI-generated output without changing the base model.
Sample answer
In a previous project, we had a document drafting tool that produced content that was technically correct but too generic for end users. We couldn’t swap the model because of cost and deployment constraints, so I focused on the surrounding system. I tightened the prompt structure, added a few-shot example set based on the best human-written outputs, and improved input preprocessing so the model received cleaner, more specific context. I also introduced a post-generation checker that flagged unsupported claims and encouraged the model to rewrite those sections. That reduced the amount of manual editing significantly. What I learned was that output quality often improves more from better orchestration than from just using a larger model. I also learned to measure quality in a user-centered way, because a technically strong answer is not useful if it doesn’t match the way the product is actually used.
Question 3
Difficulty: hard
How do you evaluate whether a generative AI feature is actually good enough to ship?
Sample answer
I don’t rely on one metric, because generative AI quality is multi-dimensional. I usually evaluate along four axes: correctness, relevance, safety, and usefulness. First, I build a representative test set from real user queries and score outputs against expected outcomes, sometimes with human reviewers and sometimes with rubric-based automated checks. Then I look at task completion, edit distance, or acceptance rate depending on the product. For RAG systems, I also measure retrieval quality and groundedness, because a good answer that isn’t supported by evidence is still a failure. I compare the model against a baseline and test edge cases, especially ambiguous prompts and adversarial inputs. Finally, I validate in production with A/B tests and user feedback loops. If the tool saves time, reduces errors, and users keep coming back to it, that’s a strong signal it’s ready. If not, I’d rather delay launch than ship something brittle.
Question 4
Difficulty: hard
Describe how you would reduce hallucinations in a large language model application.
Sample answer
I’d approach hallucination reduction as a layered problem rather than trying to solve it with one trick. First, I’d make sure the prompt is precise and includes clear instructions about uncertainty, scope, and citation behavior. Second, I’d add retrieval or structured tools wherever possible so the model can ground its response in real data instead of relying on memory. Third, I’d constrain generation with output schemas or function calling when the task allows it. I’d also implement verification steps, such as a second-pass critic or a rules-based validator for high-risk claims. On the data side, I’d review prompts and outputs to identify recurring hallucination patterns, then tune the system around those failure modes. Just as important, I’d set user expectations properly. If a product can’t guarantee accuracy in every case, it should say so clearly and provide a safe fallback. Reliability is as much about product design as model behavior.
Question 5
Difficulty: medium
How would you choose between fine-tuning a model and using prompt engineering plus retrieval?
Sample answer
I’d choose based on the kind of problem we’re solving. If the task is mostly about current knowledge, proprietary content, or frequently changing information, I’d usually start with retrieval and prompting. That gives us freshness, lower maintenance, and faster iteration. If the problem is more about consistent style, domain-specific formatting, or a repeated transformation pattern, fine-tuning may be the better option. I also think about data volume, cost, latency, and how stable the task is. Fine-tuning makes sense when we have enough high-quality examples and a clear target behavior that the base model doesn’t reproduce reliably. But I try not to fine-tune too early, because it adds operational overhead and can make debugging harder. In practice, I’d prototype both approaches on a small benchmark and compare quality, latency, and maintenance cost. The right choice is usually the one that is simplest while meeting the product requirements.
Question 6
Difficulty: medium
Tell me about a situation where you had to work with product, design, or legal stakeholders on an AI feature.
Sample answer
On one project, we were building a generative assistant that could summarize internal policy documents for employees. Product wanted the feature launched quickly, design wanted a smooth conversational experience, and legal was concerned about liability if the assistant gave incorrect advice. I helped bring everyone onto the same page by translating the technical risks into product terms. Instead of promising perfect answers, we agreed on a scoped use case: summaries with source citations and a clear disclaimer that the assistant was not a legal authority. I worked with design to make the citation experience visible but not noisy, and I worked with legal on a review workflow for high-risk topics. That collaboration actually improved the product because it forced us to define what the assistant should and should not do. I’ve found that good AI engineering is often about aligning expectations early so you can move faster later without creating rework or risk.
Question 7
Difficulty: hard
How would you build a test strategy for prompt changes in a production AI application?
Sample answer
I’d treat prompt changes like code changes, because they can alter behavior in subtle ways. My first step would be to maintain a regression suite made from real production queries, edge cases, and high-risk examples. Each test should include the expected behavior, not just the expected wording, because generative outputs can vary. I’d run the new prompt against the baseline and compare quality across dimensions like accuracy, tone, safety, and completeness. For tasks with structured output, I’d also verify schema validity and downstream compatibility. If the prompt change affects a critical workflow, I’d do a staged rollout with a small percentage of traffic and monitor user feedback, error rates, and escalation patterns. I’d also keep prompt versioning and change logs so we can trace regressions quickly. The key is to make prompt iteration measurable. Without that discipline, teams end up guessing whether a change improved things or just made the output sound better.
Question 8
Difficulty: medium
How do you handle a case where an LLM gives a confident but wrong answer in front of users?
Sample answer
I’d first look at whether the system gave the model too much freedom. Confident wrong answers often happen when the model is asked to answer beyond its evidence. In the product, I’d reduce the chance of that by improving grounding, narrowing the scope of the question, or forcing the model to cite sources before it can answer. If it’s already in production, I’d log the failure, identify the prompt and retrieval path, and patch the issue quickly. I’d also add a user-facing fallback that encourages clarification or escalation when confidence is low or evidence is weak. From a team perspective, I’d avoid blaming the model alone. Usually the real issue is the full system design: bad context, weak constraints, or no verification step. What matters most is being transparent with users and responding fast. In AI products, trust can drop quickly, but it can also be rebuilt if the team handles mistakes clearly and consistently.
Question 9
Difficulty: hard
What is your approach to optimizing latency and cost in a generative AI service?
Sample answer
I usually start with the end-to-end path, because cost and latency often come from the system architecture, not just the model itself. I’d profile retrieval, prompt construction, model inference, post-processing, and any external API calls. Then I’d look for easy wins like reducing prompt length, caching repeated queries, and using smaller models for simpler tasks. If the product has multiple use cases, I’d route requests by complexity so we don’t pay for a large model when a lightweight one is enough. For RAG systems, I’d make retrieval more selective and avoid sending unnecessary context. I’d also consider batching, streaming responses, and asynchronous workflows where possible. On the cost side, I’d track token usage by feature and user segment so we can understand what’s driving spend. The goal is not just to make the system cheaper, but to preserve quality while getting better unit economics. That balance is what makes a product scalable.
Question 10
Difficulty: easy
Why do you want to work as a Generative AI Engineer, and what makes you effective in this role?
Sample answer
I like this role because it sits at the intersection of model behavior, product design, and user trust. Generative AI is powerful, but the value only shows up when the system is designed carefully enough for real users. What excites me most is building features that feel helpful, reliable, and grounded rather than just impressive in a demo. I think I’m effective in this role because I’m comfortable moving between experimentation and production thinking. I can work through prompt design, retrieval, evaluation, and deployment, but I also pay attention to the user experience and business outcome. I’m especially careful about failure modes, because in AI work the edge cases matter as much as the happy path. I enjoy collaborating with cross-functional teams and turning vague ideas into measurable systems. For me, the best Generative AI work is not about chasing novelty; it’s about creating tools people can trust and actually want to use.
