HardAI MLTechnical screen20 min to answer

How would you evaluate a RAG pipeline end to end?

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 retrieval recall, ranking, faithfulness, task outcome.
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 retrieval recall, ranking, faithfulness, task outcome. I would select an approach only after comparing quality, latency, cost, safety, and operational complexity, then validate it with representative cases and production monitoring.

retrieval recall
ranking
faithfulness
task outcome

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 retrieval recall, ranking, faithfulness, task outcome. I would select an approach only after comparing quality, latency, cost, safety, and operational complexity, then validate it with representative cases and production monitoring.

retrieval recall
ranking
faithfulness
task outcome

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

Grade yourself

retrieval recall

weight 30%

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

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

ranking

weight 25%

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

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

faithfulness

weight 25%

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

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

task outcome

weight 20%

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

WeakMentions task outcome without implications, trade-offs, or evidence.

Then expect these follow-ups

What would your evaluation dataset contain?
Tests: depth
What if offline and online results disagree?
Tests: adaptability
How would you set the launch threshold?
Tests: adaptability

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