A financial services team is building an assistant that must solve multi-step budgeting calculations submitted by clients (e.g., 'If I save 12% of my $4,200 monthly income for 18 months, then withdraw 30%, how much remains?'). Early tests with a strong instruction-tuned LLM show the final numeric answer is frequently wrong, even though the model clearly understands the question. The engineer wants a prompt strategy to improve arithmetic accuracy without fine-tuning or adding external tools yet. Which prompt strategy should the engineer apply first?