HardTechnical screen20 min to answer

How would you decide between prompting, RAG, fine-tuning, and training a model?

1Give yourself 20 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 problem diagnosis, data, cost, maintenance.
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 problem diagnosis, data, cost, maintenance. I would select an approach only after comparing quality, latency, cost, safety, and operational complexity, then validate it with representative cases and production monitoring.

problem diagnosis
data
cost
maintenance

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 problem diagnosis, data, cost, maintenance. I would select an approach only after comparing quality, latency, cost, safety, and operational complexity, then validate it with representative cases and production monitoring.

problem diagnosis
data
cost
maintenance

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

Grade yourself

problem diagnosis

weight 30%

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

WeakMentions problem diagnosis without implications, trade-offs, or evidence.

data

weight 25%

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

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

cost

weight 25%

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

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

maintenance

weight 20%

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

WeakMentions maintenance 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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