How would you design AI-powered email writing?

Signal to interviewer

I can design a generative writing feature as an operational quality system, not just a text generation widget, by treating failures as incident inputs for product iteration.

Clarify

I would define user jobs first: cold outreach, internal updates, customer responses, and escalation emails. I would clarify acceptable autonomy, brand voice constraints, and whether usage is consumer or enterprise. I would also align on what counts as a failure: factual error, tone mismatch, compliance breach, or low actionability.

Approach

I would use an incident postmortem framework. Every severe failure gets classified by trigger, detection gap, and prevention mechanism. Product design includes intent-aware prompting, recipient context selection, confidence cues, and structured edit controls before send. The assistant would expose why language was chosen, enabling fast correction and trust building.

Metrics & instrumentation

Primary metric: first-draft acceptance rate for sent emails. Secondary metrics: median rewrite effort, time-to-send, and post-send correction incidents. Guardrails: compliance violation rate, factual inaccuracy reports, and user disablement after bad drafts. Instrumentation: prompt intent, tone preset, edit delta, send outcome, and incident tags linked to failure taxonomy.

Tradeoffs

Higher automation reduces drafting effort but increases risk of unnoticed errors. Strong policy filters reduce legal risk but can make suggestions feel rigid. Rich context improves relevance but requires tighter permission controls.

Risks & mitigations

Risk: hallucinated specifics in client-facing mail; mitigate with fact-check prompts and source attachment checks. Risk: tone drift across departments; mitigate with team-level style libraries and approval templates. Risk: silent quality regressions after prompt updates; mitigate with canary rollout and incident-triggered rollback.

Example

In a B2B sales organization, the assistant drafts renewal outreach using account context and approved pricing language. If a draft triggers prohibited-claim detection, it forces a safer rewrite path and logs the event for postmortem review.

90-second version

Design AI email writing as a reliability loop: generate with context, require lightweight user control, instrument sends, and run postmortems on severe misses. Improve quality through failure taxonomy and targeted fixes so trust rises over time.