This is one of those questions where you can go a lot of directions and that's kind of the problem.
Start by clarifying the LLM's specific use case and objectives, then propose a mix of automated metrics, human evaluations, and business KPIs. Emphasize the importance of aligning evaluation with product goals and iterating based on feedback.
Pro tip: Highlight the trade-offs between automated metrics and human judgment, and suggest a tiered evaluation system that scales from offline benchmarks to online A/B tests. This shows you understand both technical rigor and product impact.
Clarify the LLM's purpose (e.g., content recommendation, chat, summarization) and the key performance indicators that matter for the product. This ensures evaluation is aligned with business goals.
Choose appropriate automated metrics such as perplexity, BLEU, ROUGE, BERTScore, or task-specific accuracy. For generative tasks, consider diversity and coherence metrics.
Design human evaluation protocols (e.g., Likert scales, pairwise comparisons) to assess qualities like relevance, fluency, and safety. Use crowdworkers or domain experts as needed.
Track product-level metrics (e.g., user engagement, retention, click-through rate) through A/B tests or online experiments to measure real-world impact.
Continuously analyze results, identify gaps, and refine both the model and evaluation strategy. Use feedback loops to improve performance over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.