Product design question

How would you improve Claygent to increase data-enrichment accuracy?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Tests product design for improving the reliability of an AI research agent used for business critical data enrichment.

Define accuracy precisely for this use case: the enriched data field, like a company's employee count or a contact's job title, actually matches reality, not just looks plausible.
Identify the current failure modes: Claygent may pull outdated data, misattribute information to the wrong entity, or hallucinate a plausible sounding but unverified answer.
Prioritize source transparency: show exactly which source Claygent used for each enriched field, so users can spot check questionable results quickly.
Add confidence scoring per field: flag low confidence enrichments distinctly, so users know which fields to verify before trusting them in a sales or marketing workflow.
Build a feedback loop: let users flag incorrect enrichments, feeding that signal back into source prioritization and model evaluation over time.
Confirm with the interviewer whether the priority is improving accuracy on a specific data type, like firmographic data, or reducing hallucination broadly across all research tasks.

Prioritizes source transparency per field, letting users verify enrichment quickly rather than trusting a black box output, which matters greatly in a GTM workflow feeding sales outreach.
Proposes confidence scoring so users can triage which enrichments need manual verification instead of treating all outputs as equally reliable.
Builds a feedback loop from user corrections back into source prioritization, improving accuracy over time rather than treating each run as independent.
Distinguishes specific failure modes, outdated data, misattribution, and hallucination, since each needs a different fix rather than one generic accuracy improvement.

Treating accuracy as one undifferentiated problem without naming the specific failure modes, like misattribution versus hallucination.
No source transparency, leaving users unable to verify a questionable enrichment without redoing the research themselves.
No feedback loop, so the same errors could recur without the system learning from user corrections.

How would you measure Claygent's accuracy rate across different data types?
What would you do if two sources gave conflicting information for the same field?
How would you help a user quickly spot check a batch of enriched records?