EasyTechnical screen5 min to answer

Define hallucinations in LLMs and explain how you would reduce them.

Asked at

OpenAI

Anthropic

Mastercard

1Give yourself 5 minutes

2Answer out loud, not in your head

3Then compare with the answer below

Stuck? Show a way to structure it+

01Clarify the objective, user, constraints, and acceptable failure modes.
02Structure the response around taxonomy, retrieval, constraints, evaluation.
03Compare credible alternatives and state the decision criteria.
04Finish with validation, monitoring, and what would change the decision.

Reference answer

strong answer

I would first define the outcome and acceptable error boundary. I would explicitly cover taxonomy, retrieval, constraints, evaluation. I would select an approach only after comparing quality, latency, cost, safety, and operational complexity, then validate it with representative cases and production monitoring.

taxonomy
retrieval
constraints
evaluation

Must be adapted to the interviewer’s scenario
Would benefit from concrete numbers and personal examples

alternative answer

A second valid approach is to begin with the highest-risk assumption and test it cheaply. I would explicitly cover taxonomy, retrieval, constraints, evaluation. I would select an approach only after comparing quality, latency, cost, safety, and operational complexity, then validate it with representative cases and production monitoring.

taxonomy
retrieval
constraints
evaluation

Must be adapted to the interviewer’s scenario
Would benefit from concrete numbers and personal examples

Grade yourself

taxonomy

weight 30%

StrongExplains taxonomy concretely and ties it to a decision or measurable outcome.

WeakMentions taxonomy without implications, trade-offs, or evidence.

retrieval

weight 25%

StrongExplains retrieval concretely and ties it to a decision or measurable outcome.

WeakMentions retrieval without implications, trade-offs, or evidence.

constraints

weight 25%

StrongExplains constraints concretely and ties it to a decision or measurable outcome.

WeakMentions constraints without implications, trade-offs, or evidence.

evaluation

weight 20%

StrongExplains evaluation concretely and ties it to a decision or measurable outcome.

WeakMentions evaluation without implications, trade-offs, or evidence.

Then expect these follow-ups

What assumption is most fragile?
Tests: depth
What alternative did you reject and why?
Tests: adaptability
How would you validate this in production?
Tests: adaptability

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