Question 1
Difficulty: medium
How do you approach designing a generative AI solution for a business problem when the requirements are still unclear?
Sample answer
I start by forcing clarity on the business outcome before touching the model. In practice, I’ll meet with stakeholders to define the user, the decision they need to make, the risks, and what success looks like in measurable terms. Then I break the problem into smaller pieces: is this a retrieval problem, a summarization problem, a classification problem, or truly a generation problem? From there I usually prototype the simplest path first, often with prompt engineering plus retrieval, before considering fine-tuning. I also pay close attention to guardrails, latency, and cost because those constraints often shape the architecture more than the model choice does. A strong solution engineer needs to translate ambiguity into a testable plan, so I like to create a quick baseline, define evaluation criteria, and iterate with real user feedback. That keeps the project grounded and prevents overengineering.
Question 2
Difficulty: easy
Tell me about a time you had to persuade a team to use a simpler AI approach instead of a more advanced one.
Sample answer
I’ve found that teams often get excited about the newest model when a simpler solution would deliver faster and more reliably. In one project, the initial request was to fine-tune a large model for internal support responses. After reviewing the use case, I realized most of the value came from giving the model access to current documentation, policies, and product notes. I proposed a retrieval-augmented approach with careful prompt design and citation requirements instead of fine-tuning. I showed a proof of concept that answered more accurately on recent content and was easier to update. I also walked the team through the operational benefits: lower maintenance, faster iteration, and less risk of baking outdated knowledge into a model. Once they saw the evaluation results side by side, they were comfortable switching direction. That experience reinforced for me that the best AI solution is usually the one that solves the problem cleanly, not the one with the most complexity.
Question 3
Difficulty: hard
How would you evaluate whether a RAG system is performing well?
Sample answer
I evaluate a RAG system on several layers, not just answer quality. First, I measure retrieval performance: whether the system is pulling the right documents, whether the top results are relevant, and whether the context window is being used efficiently. Then I look at generation quality: factual correctness, completeness, tone, and whether the answer actually uses the retrieved evidence. I also check citation accuracy if the product requires it. For a practical evaluation loop, I like to build a golden set of queries that reflect real user behavior, including ambiguous and adversarial prompts. From there, I use a mix of human review and automated checks to compare versions. Latency, token usage, and failure rates matter too, because a system that is accurate but too slow or expensive won’t scale. In production, I’d monitor drift, unanswered queries, and low-confidence retrieval patterns so the system can keep improving after launch.
Question 4
Difficulty: medium
Describe how you would handle hallucinations in a customer-facing generative AI application.
Sample answer
I treat hallucinations as a product and systems problem, not just a model problem. The first step is to reduce opportunities for unsupported generation by grounding the model in trusted sources through retrieval, structured inputs, and clear system instructions. Next, I add constraints around what the model is allowed to say, especially for regulated or high-risk domains. If the model cannot verify an answer, I prefer it to say so and route the user to a human or an authoritative source. On top of that, I use evaluation sets that include tricky prompts designed to trigger unsupported claims. In production, I monitor for patterns like repeated incorrect answers, overconfident language, or answers without evidence. If needed, I’ll add post-generation validation, such as policy checks or fact verification against the source data. My goal is not to eliminate every mistake, because that’s unrealistic, but to make the system predictable, transparent, and safe enough for its intended use.
Question 5
Difficulty: hard
What is your process for choosing between prompt engineering, RAG, fine-tuning, and building custom tools or agents?
Sample answer
I choose based on the problem shape, not based on what sounds most advanced. If the task depends mostly on instruction following, formatting, or tone, I start with prompt engineering because it is fast and low risk. If the model needs access to fresh or domain-specific information, I move to RAG so I can keep the knowledge current and auditable. I consider fine-tuning when the task is consistent, the examples are strong, and the desired behavior is hard to achieve with prompting alone, such as a specialized style or classification pattern. I use tools or agents when the model needs to take actions, call APIs, or coordinate multi-step workflows. I also think about governance, latency, and cost. For example, if a fine-tuned model would still need retrieval and tool use, I’d ask whether the added complexity is worth it. My default is to build the simplest version that can be measured, then add sophistication only when the evidence supports it.
Question 6
Difficulty: easy
How do you work with product managers, data engineers, and security teams on an AI project?
Sample answer
I see the role as a translator between groups that care about different things. With product managers, I focus on the user experience, the target metric, and the scope of the first release. With data engineers, I align on source systems, ingestion quality, metadata, and how often the knowledge base needs to refresh. With security teams, I’m very explicit about data boundaries, access control, logging, retention, and whether any sensitive information can reach the model provider. I try to bring each group into the design early, not after the architecture is already locked. That avoids a lot of rework later. I also like to document assumptions and risks in plain language so no one has to guess what the system is doing. In my experience, the projects that move fastest are the ones where technical decisions are tied to business goals and where each stakeholder understands how their piece affects the final system.
Question 7
Difficulty: medium
A client wants an AI assistant that answers questions from internal documents, but the documents are messy and constantly changing. What would you do?
Sample answer
I would not start by building the assistant immediately. First, I’d assess the document landscape: file types, ownership, update frequency, duplication, and whether the source of truth is clear. If the documents are messy, the assistant will reflect that mess unless we put structure in place. My first deliverable would likely be a content pipeline that ingests documents, cleans them, chunks them appropriately, and attaches metadata like source, date, and department. I’d also work with the client to identify which documents are authoritative and which should be excluded or treated as secondary. For the assistant itself, I’d use RAG with citations so users can verify answers. I’d probably start with a narrow set of high-value document types and expand once the retrieval quality is stable. That approach keeps the project practical and gives the client value while the information governance improves in parallel.
Question 8
Difficulty: hard
What metrics would you track after launching a generative AI feature in production?
Sample answer
I’d track a mix of product, quality, and operational metrics because one category alone never tells the full story. On the product side, I’d look at adoption, repeat usage, task completion, and user satisfaction. On the quality side, I’d monitor answer accuracy, groundedness, citation correctness, and human escalation rates. If the feature supports customer support or internal operations, I’d also measure time saved and reduction in manual effort. Operationally, I care about latency, token cost, error rates, retrieval failure rates, and uptime. I’d also watch for safety issues such as policy violations or sensitive data leakage. The most useful setup is usually a dashboard that combines real-time operational signals with periodic human evaluation. That way you can see both immediate failures and slower quality drift. I like to define alert thresholds early so the team knows what constitutes normal variation versus an issue that needs intervention.
Question 9
Difficulty: medium
Tell me about a time you had to debug a generative AI system that was behaving unexpectedly.
Sample answer
In one project, the assistant started giving inconsistent answers even though the underlying model had not changed. I approached it like a system debugging problem rather than assuming the model was the issue. First, I checked recent changes in the prompt, retrieval pipeline, and document index. It turned out the ingestion job had started pulling in duplicate and outdated content, which was confusing retrieval and pushing irrelevant passages into the prompt. I compared the answers against the retrieved context and confirmed that the model was often doing the right thing with bad inputs. We fixed the ingestion rules, improved deduplication, and tightened the metadata filtering. After that, I added a small evaluation set to catch regressions whenever the content pipeline changed. The main lesson for me was that generative AI failures often come from the surrounding system, not just the model itself. Good debugging means tracing the full path from user query to retrieved context to final response.
Question 10
Difficulty: hard
How do you ensure a generative AI solution is safe, compliant, and enterprise-ready?
Sample answer
I think about enterprise readiness from day one, not as a final checklist. I start with data governance: what can enter the system, where it is stored, how long it is retained, and who can access logs or outputs. Then I design for least privilege, meaning the assistant only sees the information it truly needs. If the use case involves sensitive content, I work closely with legal, compliance, and security teams to define acceptable behavior and escalation paths. I also add protective layers such as content filtering, output validation, and clear refusal behavior for disallowed requests. From an operational standpoint, I want auditability, version control for prompts and models, and reproducible evaluations. I also make sure users understand the limitations of the system so they don’t over-trust it. To me, enterprise-ready means the solution is useful, measurable, supportable, and defensible if something goes wrong. That balance is what builds long-term trust with customers and internal teams.
