LLM Testing Interview Questions: RAG, Evaluation and Safety

Testing an AI product needs a different way of thinking, and interviewers know it. These LLM testing interview questions cover why old approaches fall short and how you would design something better. Answer in terms of evidence and risk, and use the evaluation work you did in Level 5 as your examples.

📖 What You’ll Learn

How to explain why AI testing is different
Questions on evaluation and golden datasets
Questions on RAG, agents and safety
How to make a release decision

💡 What LLM Testing Interview Questions Look For

Explain Like I’m 5: Judging a Drawing

Imagine: A maths answer is right or wrong. A drawing is judged on several qualities at once.

What it really means: Many AI answers need scoring against several qualities.

In testing: Check accuracy, relevance and safety instead of one exact string.

Area	They want to hear
Non-determinism	How you test outputs that vary
Evaluation	How you measure quality
RAG	How you separate retrieval from generation
Safety	How you test harmful behaviour
Release	How you decide that it is good enough

🔍 Questions on the Fundamentals

Why can’t you just assert an exact output?

The same input can produce different valid wording. I assert required facts, structure and safety properties, and I score quality across a dataset.

How do you test a non-deterministic system?

I run each case several times, use property checks and semantic scoring and judge pass rates. I also keep the model, prompt and settings recorded for each run.

What is a golden dataset?

It is a reviewed set of inputs and expected qualities. It makes changes comparable over time. I build it from real scenarios and include hard cases, as in Golden Dataset for LLM Evaluation.

How would you test a chatbot?

I would define its purpose and limits, then test accuracy, refusals, tone, safety, data handling and regressions. I would combine automated evaluation with human review.

🧩 Questions on Evaluation

Explain Like I’m 5: Marking with a Rubric

Imagine: A teacher marks an essay with a list of what counts, so that two teachers give similar marks.

What it really means: A rubric makes judgement more consistent.

In testing: Define what a score of 0, 1 and 2 means.

What metrics would you use for LLM quality?

It depends on the product. Common ones are faithfulness, relevance, completeness, hallucination rate and refusal accuracy. I add cost and latency, and explain them in LLM Evaluation Metrics for Testers.

Can you use an LLM to judge another LLM?

Yes, as a scalable first pass, with a clear rubric. I check the judge against human labels, because it can be biased or wrong.

How do you detect quality drift?

I run a fixed evaluation set on a schedule and after any change, then compare against a baseline. Providers can change model behaviour.

What would you do if your pass rate was high but users complained?

I would look at real conversations and add the failures to the dataset. My evaluation set clearly did not represent real use.

🛠️ Questions on RAG and Agents

How do you test a RAG system?

I test retrieval and generation separately. I check that the right documents are found, then that the answer is grounded in them. See Testing RAG Applications.

An answer is wrong. How do you find out why?

I check whether the right content was retrieved. If it was missing, the problem is retrieval or the data. If it was present, the problem is generation or the prompt.

What is different about testing an AI agent?

Besides the final answer, I test its tool choices, arguments, stop conditions and failure handling. I use fake tools and models to make these repeatable. The approach is in Testing AI Agents.

📌 Questions on Safety

Explain Like I’m 5: A Stranger at the Door

Imagine: A clever stranger may have a very good story. Your rules stay the same.

What it really means: Hostile input tries to talk a system out of its rules.

In testing: Test instructions hidden in documents and web pages.

What is prompt injection, and how do you test for it?

It is input designed to override a system’s instructions. I test direct attempts and instructions hidden in retrieved content, on systems I am authorised to test. Details are in Prompt Injection Testing.

How do you test for bias?

I compare results for paired inputs that differ only in a sensitive attribute, then review the differences with people who understand the context.

How do you test data leakage?

I use seeded canary values and attempt to extract them. I also check logs and traces for sensitive content.

Never test attacks without permission

Only run security and injection tests on systems that you own or are authorised to test. Say this in interviews too, because it shows professional judgement.

🌍 Real-Life Example: The Perfect Score

A team reports 98% on its evaluation, and users are still unhappy. A tester discovers that the dataset contains only easy questions written by the developers. Adding real and difficult questions drops the score sharply, and the team finally starts fixing the right problems.

🧪 Try It Yourself

Explain non-determinism in thirty seconds.
Design five test cases for a support chatbot.
Write a 0 to 2 rubric for one quality.
Decide how you would investigate a wrong answer.
Run the prompt below.

📋 Ready-to-Copy Prompt

You are a senior AI quality engineer interviewing me for an LLM testing role.

Product: [describe an AI feature with no confidential detail]

Ask me one question at a time on evaluation, RAG, agents, safety and release
decisions. Add follow-up questions when my answers are vague.

Then score me against a 0 to 2 rubric, and name the two topics I should
study next.

⚠️ Common Mistakes

Trying to assert exact wording
Quoting a pass rate without describing the dataset
Treating LLM judges as always correct
Forgetting cost and latency
Testing attacks without permission

🎯 Key Takeaways

Test properties, not exact strings.
A fixed, realistic dataset makes change visible.
Separate retrieval from generation.
Tie every release decision to risk.

🎤 Interview Questions

1. How would you decide whether an LLM feature can be released?

Model answer: I would agree thresholds by risk, check evaluation results and safety cases, review failures by hand and plan monitoring and rollback. A single average is never enough.

2. How do you test a model upgrade?

Model answer: I run the same dataset on both versions, compare quality, safety, cost and latency, then review the differences before gradually rolling it out.

3. How do you build a dataset with no production data?

Model answer: I start with requirements, expert-written cases and synthetic edge cases. I add real anonymised failures later, where policy allows it.

4. How would you monitor an LLM in production?

Model answer: I track errors, latency, cost, refusals and user feedback, and sample outputs for review. I also run a scheduled evaluation to detect drift.

5. What is your biggest concern with AI quality today?

Model answer: Fluent answers can hide errors, so teams may trust them too easily. I address that with evidence, human review and honest measurement.

📝 Assignment

Write a test strategy for a fictional company knowledge assistant. Include scope, a dataset plan, metrics, safety cases, release criteria and monitoring. Limit it to two pages.

❓ FAQ

Do I need to know how to train models?

Usually not for QA roles. Understanding how models behave and how to evaluate them matters more.

Which evaluation tool should I learn?

Learn the concepts first, then one tool such as the one covered in LLM Regression Testing in CI.

🚀 Next Steps

Well done on finishing this lesson. Complete the assignment, then continue with AI System Design Interview: Design a QA Agent Step by Step.