Improve LLM reasoning for a domain task

Q: Improve LLM reasoning for a domain task

This question evaluates competency in end-to-end LLM system design for improving multi-step reasoning, including task specification, data construction, prompt and inference strategies, fine-tuning/post-training choices, retrieval and tool integration, evaluation, and operational trade-offs.

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You are building an LLM-powered product for a domain-specific task that requires multi-step reasoning. The base model does reasonably well on easy examples, but it often fails on harder cases that require decomposition, intermediate verification, or tool use.

Design an end-to-end plan to improve the model's performance on this reasoning-heavy task. Your answer should cover:

how you would define the task and success metrics
how you would collect or construct training data
prompt and inference-time techniques you would try first
whether and how you would fine-tune or post-train the model
when to use retrieval, external tools, verifiers, or self-checking
how you would evaluate quality offline and online
key latency, cost, and safety trade-offs
how you would debug failures after deployment

Improve LLM reasoning for a domain task

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