HardSystem designSystem design30 min to answer

Design a language model that minimizes harmful outputs while remaining useful and expressive.

Asked at

Anthropic

1Give yourself 30 minutes

2Answer out loud, not in your head

3Then compare with the answer below

Stuck? Show a way to structure it+

01Clarify objective, users, constraints, and failure tolerance.
02Cover helpfulness-harmlessness trade-off, training objective, evaluation, deployment controls in a causal structure.
03Compare at least two credible approaches.
04Specify evaluation, rollout, monitoring, and rollback.

Reference answer

strong answer

I would begin by defining the target outcome and acceptable failure boundary. For Alignment design, I would analyze helpfulness-harmlessness trade-off, training objective, evaluation, deployment controls, compare alternatives on quality, latency, cost, and risk, and validate on representative slices before a monitored rollout.

helpfulness-harmlessness trade-off
training objective
evaluation
deployment controls

Requires scenario-specific numbers
Should include relevant personal experience

alternative answer

I would start with the riskiest assumption in Alignment design, create the cheapest discriminating experiment, then use the evidence to choose an architecture and release threshold.

experimentation
risk-first sequencing
decision clarity

Needs deeper implementation detail

Grade yourself

helpfulness-harmlessness trade-off

weight 30%

StrongExplains helpfulness-harmlessness trade-off with mechanisms, trade-offs, and validation.

WeakNames helpfulness-harmlessness trade-off without operational implications.

training objective

weight 25%

StrongExplains training objective with mechanisms, trade-offs, and validation.

WeakNames training objective without operational implications.

evaluation

weight 25%

StrongExplains evaluation with mechanisms, trade-offs, and validation.

WeakNames evaluation without operational implications.

deployment controls

weight 20%

StrongExplains deployment controls with mechanisms, trade-offs, and validation.

WeakNames deployment controls without operational implications.

Then expect these follow-ups

Which assumption is most likely to invalidate your approach?
Tests: depth
What would you measure offline and in production?
Tests: evaluation
How would your answer change with one-tenth the data or compute?
Tests: adaptability

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