This white paper outlines how prompt engineering optimizes large language model performance by crafting precise, context-rich inputs. It contrasts prompt engineering (accuracy, iteration, context) with prompt design (clarity, ethics, inclusivity), shows good vs. bad prompt examples, and quantifies business gains like 10–30% accuracy boosts. It details a prompt lifecycle—design, implementation, evaluation, refinement—and metrics from F1 scores to fairness checks. A six-step optimization framework stresses specificity, context, examples, and iteration. Effective prompting improves efficiency, reduces risk, and enhances decision-making.

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