Start by clarifying the task and success metrics, then propose a holistic pipeline covering data, training, inference, evaluation, and cost. Emphasize iterative improvements and trade-offs, and highlight how you would measure and optimize each component.
Pro tip: Anchor your answer in a concrete example (e.g., math word problems) to make trade-offs tangible, and always tie improvements back to measurable business impact like accuracy per dollar or latency.
Define the task, success criteria (e.g., exact match, pass@k), and constraints (latency, budget). Identify failure modes and baseline performance.
Curate high-quality chain-of-thought data, augment with synthetic examples, and fine-tune with techniques like SFT, RLHF, or DPO. Consider curriculum learning and data filtering.
Apply prompting strategies (few-shot, CoT), self-consistency, tree-of-thought, or tool integration. Use verifiers or reward models to select best outputs.
Build a robust eval set with diverse difficulty, use automated metrics and human review. Analyze errors to guide next iterations.
Quantify trade-offs between accuracy, latency, and cost. Optimize via model distillation, caching, or adaptive computation. Prioritize high-impact changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.