← Perplexity AI Interview Insights
Start by clarifying the product's objective and the AI's role, then define success metrics that balance user value, business impact, and model performance. Emphasize a layered approach: north-star metric, primary metrics, and guardrail metrics, tailored to AI-specific challenges like accuracy, latency, and user trust.
Pro tip: For AI products, always include a 'quality' metric that directly measures the AI's output (e.g., answer relevance, factuality) alongside traditional engagement metrics, because poor AI quality can silently erode user trust even if engagement looks healthy.
Identify the core user problem and how AI uniquely solves it. Determine whether AI is the core value proposition or an enhancer, as this shapes metric priorities.
Choose a single metric that best captures sustained user value and aligns with business goals, such as 'weekly active users who find answers' for a search AI.
Pick metrics for user engagement (e.g., retention, frequency), business impact (e.g., conversion, revenue), and AI quality (e.g., accuracy, relevance, latency).
Define metrics that ensure the AI doesn't cause harm, such as hallucination rate, bias, or user trust (e.g., thumbs-down rate).
Use A/B tests and qualitative feedback to refine metrics, ensuring they are actionable and predictive of long-term success.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.