Interview
Questions
Perplexity Search Engineering: Freshness and Grounded Answers
Practice search-index priorities, retrieval-to-answer diagnosis, and evidence for answer-quality decisions. Independent speaking.app practice based on public Perplexity requirements and engineering material.
Practice these questions directly. An editable copy is yours to change; the original stays unchanged.
Tell me about a RAG system you built or improved. What challenges did you face with retrieval quality and how did you address them?
Your search index has a fixed crawling budget. How would you decide how much of it to spend refreshing existing pages?
Search retrieves relevant pages, but the generated answer gives an outdated fact. How would you determine whether the failure came from crawling, parsing, snippet selection, ranking, or answer generation?
How have you evaluated LLM outputs for quality in production? What metrics or approaches did you use?
An AI assistant’s offline scores improve, but a live experiment shows no improvement in completed user tasks and more complaints about citations. How would you investigate the mismatch and decide whether to ship, revise, or stop the change?
Describe a time you had to manage token costs while maintaining output quality. What was your approach?
Tell me about a time you worked with cross-functional teams like product or design. How did you collaborate effectively?