Start by defining what 'quality' and 'effectiveness' mean for the specific generative AI product, then propose a layered evaluation framework that combines automated metrics, human evaluation, and business KPIs. Emphasize the importance of aligning technical metrics with user and business outcomes, and discuss trade-offs between different evaluation methods.
Pro tip: Anchor your answer in LinkedIn's context by referencing how generative AI could enhance member experience (e.g., content suggestions, profile optimization) and tie metrics to engagement and trust. Show you understand that offline metrics don't always correlate with online success, so advocate for continuous A/B testing and guardrail metrics.
Clarify the purpose of the generative AI system and what success looks like from user, business, and technical perspectives. Identify key stakeholders and their priorities.
Choose a mix of automated metrics (e.g., BLEU, ROUGE, perplexity), human evaluation criteria (e.g., relevance, fluency, safety), and business KPIs (e.g., engagement, conversion, retention).
Determine how to collect data: offline test sets, human annotation, online A/B tests. Ensure statistical rigor and account for biases.
Compare results against baselines, identify gaps, and prioritize improvements. Use qualitative feedback to complement quantitative metrics.
Set up ongoing monitoring for model drift, safety, and fairness. Establish guardrail metrics and a feedback loop for continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.