citations

The failure every legal AI product fears. The interviewer wants a response that makes invented citations structurally impossible to present as real, not a promise to improve the model.

People mostly find their own files by navigating, using search as a last resort. Keep navigation strong, bring related notes up while people write, and offer cited Q&A for when they can't remember where something is.

Citations are the main trust device in AI answers, and published audits of shipped search engines find many that do not support the sentence they sit on. The interviewer wants citations at the right granularity, checkable in seconds, and honest about what they support.

Search visits with an AI summary produced 40% fewer outbound clicks in one large study. Decide which questions deserve a complete answer, which deserve a doorway and how you will measure the difference.

Search plus generation at consumer scale. The design has to deliver fresh, cited answers in a couple of seconds, resist manipulated pages, and keep cost per query viable.

A lawyer will not trust a summary. Design clause extraction, playbook comparison and cited deviations, with a measured miss rate on the clauses that carry liability.

Research agents are judged on whether an analyst can trust and reuse the output. The design centres on source quality, verifiable citations and showing the plan before spending ten minutes.

Short-answer benchmarks say little about ten-page reports. Check each statement against the source it cites, score coverage against an expert outline, test balance on contested topics and measure whether users act without redoing the work.

RAG is the most common live-build topic. Strong candidates choose the simplest approach that works for 30 documents, make citations real, and test retrieval and answers separately.