Prompt engineering is the science of testing different prompts to optimize a language model's performance. It involves using input data, exemplars, instruction, indicators, and context to craft effective prompts. Strategies for prompt engineering include being empirical, starting simple, being specific and direct, using exemplars, and avoiding complexity if possible.
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