A financial services company uses a generative AI assistant to help analysts evaluate complex loan applications that require multi-step logic (verifying income thresholds, then debt ratios, then risk tiers). Analysts report the model often jumps to a final recommendation but gets intermediate calculations wrong. Without changing the model or adding new data sources, which prompt engineering technique would MOST improve the accuracy of these multi-step evaluations?