What you need to know
Practise Generative AI Engineer interview answers on grounding, prompt design, evaluation, injection risks, human review and quality-cost trade-offs through ten practical questions.
Practise Generative AI Engineer interview answers on grounding, prompt design, evaluation, injection risks, human review and quality-cost trade-offs through ten practical questions.
These are original practice questions, not questions reported from a particular employer. Every scenario below is hypothetical. Use the answers to understand the reasoning, then explain the decision in your own words and connect it to work you actually did.
How to practise
First answer aloud without reading. State the goal, assumptions, proposed approach, checks and trade-offs. Then compare your reasoning with the answer, tackle the follow-up and write down one gap to practise. Aim for a clear two-minute initial explanation; expand when asked. This timing is a practice suggestion, not an interview rule.
Role-based questions and detailed answers
1. How do you define quality for a generation task?
Answer: Translate the user task into observable criteria: required facts, prohibited claims, completeness, format and appropriate abstention. Build examples covering normal and difficult cases and write a scoring rubric before comparing prompts. Separate factual correctness from style. Where judgement is subjective, document reviewer disagreement rather than presenting every preference as an objective quality score.
Hypothetical scenario: A summary sounds polished but removes the exception that determines whether a customer qualifies.
Follow-up: Which criterion should outweigh fluent wording in this case?
Common mistake: Evaluating generation using only whether the text sounds professional.
2. When would you choose prompting, retrieval or fine-tuning?
Answer: Start with prompting and a measured baseline. Retrieval is useful when answers need accessible, changing source information. Fine-tuning may help repeatable behaviour or task adaptation when suitable examples and evaluation exist, but it is not a dependable live database. Choose the smallest intervention that addresses the observed failure and respect the data's access and usage constraints.
Hypothetical scenario: A policy assistant must answer from documents that change weekly and differ by employee permissions.
Follow-up: Why would training on every policy not replace permission-aware retrieval?
Common mistake: Using fine-tuning as the default solution for missing current facts.
3. How should a grounded assistant handle unsupported details?
Answer: Require a distinction between supplied evidence and inference. If necessary information is absent, ask a clarifying question or say it cannot be established from the available material. Validate important claims against retrieved passages and evaluate abstention on deliberately unanswerable cases. A citation-looking marker is insufficient unless its referenced passage actually supports the statement.
Hypothetical scenario: A source gives a product launch month but no exact date, and the model invents a day.
Follow-up: How would you score a cautious answer compared with a confident invention?
Common mistake: Assuming every answer with a citation is grounded.
4. Does lowering temperature make answers factual?
Answer: Lower randomness can make outputs more repeatable under some settings, but repeatability is not truth. A model can consistently produce the same unsupported statement. Treat generation settings as experimental variables, keep task-specific factual checks and record the configuration. Also distinguish output limits from quality controls: truncating an answer may remove required information rather than improve reliability.
Hypothetical scenario: A deterministic extractor repeatedly assigns a location absent from the employer's posting.
Follow-up: What would actually test whether the location is supported?
Common mistake: Claiming zero temperature eliminates hallucinations or guarantees identical outputs everywhere.
5. How do you use an LLM judge responsibly?
Answer: Give the judge an explicit rubric and sufficient evidence, then compare its assessments with a manually reviewed sample. Test sensitivity to answer order, verbosity and misleading instructions inside candidate answers. Keep disagreement visible and use independent factual checks where possible. Judge scores are fallible evaluation signals, not automatic proof that a system is safe or correct.
Hypothetical scenario: A long, confident answer receives a higher score than a short answer containing the necessary facts.
Follow-up: How would you test whether the judge rewards verbosity?
Common mistake: Accepting a judge score without checking its agreement with the intended rubric.
6. Why is prompt injection an application concern?
Answer: Retrieved documents and user text can contain instructions that conflict with the intended task. Separate untrusted content from control instructions and enforce access, tool scope and validation outside the model. Test malicious examples and constrain side effects. Prompt wording can help, but cannot replace backend authorization or approval for sensitive actions.
Hypothetical scenario: A retrieved page tells the assistant to export private account data to an external address.
Follow-up: Which backend boundary must hold even if the model follows that instruction?
Common mistake: Presenting a stronger system prompt as a complete security boundary.
7. How would you reduce cost without hiding quality loss?
Answer: Measure tokens, repeated work, model time and validation failures per completed task. Consider shorter relevant context, reuse of authorized stable results, smaller models for suitable cases and bounded outputs. Compare savings against a fixed quality rubric and difficult slices. Include retries and review cost; cheaper individual calls may produce a more expensive end-to-end workflow.
Hypothetical scenario: A small model costs less per request but needs repeated repairs for missing mandatory fields.
Follow-up: Which unit of cost best represents the user's completed outcome?
Common mistake: Comparing only advertised per-token prices while ignoring retries and review.
8. How do you process documents longer than the context budget?
Answer: Preserve task-relevant structure and source identity while reducing boilerplate or splitting at meaningful boundaries. Track which sections were included and never silently treat a truncated document as complete. If combining partial extractions, reconcile conflicts and retain evidence pointers. Evaluate information near boundaries and in omitted sections, because fitting the token budget alone does not establish coverage.
Hypothetical scenario: A long career page places the work-authorization condition after a large navigation and benefits section.
Follow-up: How would you detect that a required condition was never read?
Common mistake: Keeping only the beginning of every page and assuming it contains all requirements.
9. What makes a useful human-review workflow?
Answer: Show the proposed output, supporting evidence, uncertainty and the exact action being approved. Prioritize cases by consequence and ambiguity rather than asking reviewers to approve everything. Record decisions and allow correction without losing provenance. Measure whether review catches meaningful errors; a button that encourages automatic acceptance adds friction without establishing reliable oversight.
Hypothetical scenario: A reviewer must confirm an extracted salary range but sees neither currency nor the source paragraph.
Follow-up: What information is essential before their approval is meaningful?
Common mistake: Equating the existence of an approval button with effective human oversight.
10. How should prompt changes be released?
Answer: Version prompts together with model settings, schemas and evaluation cases. Compare changes against the current version, especially on known failures and important output fields. Investigate regressions before a controlled rollout and retain a rollback path. A successful new example does not justify release if it silently breaks previously supported requests.
Hypothetical scenario: A prompt fixes missing skills but starts rewriting job titles instead of preserving them.
Follow-up: Which regression test would you add before accepting the change?
Common mistake: Editing the production prompt repeatedly without versioning or evaluation.
Turn practice into evidence
Create a document-grounded assistant with a written acceptance rubric. Include answerable, ambiguous and unsupported requests. Compare a prompt change on a fixed test set, record unsupported claims and show how a reviewer sees the exact source evidence before accepting an answer.
Score each response informally: 0 if you cannot explain it, 1 for a definition, 2 for an approach with checks, and 3 for justified trade-offs plus a failure-recovery plan. This is a self-study rubric, not a validated hiring assessment. Revisit weak answers and never invent project outcomes or experience.
Career roadmap and related jobs
Build the underlying skills with the Generative AI Engineer career guide. For a shorter starting point, read AI interview answer frameworks.
Explore relevant live jobs. Listings are filtered by Generative AI and can include adjacent roles. Read the employer's requirements and original posting before applying. Use one listing to select the questions most relevant to its responsibilities.
Methodology
Editorial synthesis of cited sourcesAI-assisted original educational practice material. These are not actual employer interview questions, reported interview experiences or hiring guarantees. Answers, hypothetical scenarios, follow-ups and self-assessment advice are original editorial synthesis. Official Google references support background concepts, not endorsements of these answers. Related jobs use a specialization filter and may include adjacent roles; availability changes. References checked 17 October 2026.
Read our editorial and research policy →Sources
- Structured outputs
Google AI for Developers · Checked 17 October 2026
- RAG Engine overview
Google Cloud · Checked 17 October 2026
- Function calling
Google AI for Developers · Checked 17 October 2026