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Art and science to design input texts (prompt) to get the desired output from artificial intelligence models.
Prompt engineering is the art and science of the art and science that is conscious and systematic design of input texts (prompt) in order to get output in the desired quality and accuracy, especially from major language models. The behavior of a model largely depends on the clarity, context and structure of the instruction given to him; while the same task is uncertain or incorrectly answering with a poorly designed prompt, it can produce much more hit results with a well-designed prompt. Prompt engineering covers basic principles, such as giving clear and specific instructions, assigning a role or identity to the model (" Reply like a trained law consultant"), specifying the desired output format and providing the required context.
Among the more advanced techniques, zero-shot promptinging (defining direct task without example), few-shot prompting (provide the pattern of the model by showing several examples) and chain-of-thought prompting (the model can increase accuracy by encouraging step-by-step execution). Prompt engineering is a much faster, cheap and accessible method compared to fine-tuning because it only optimizes input, without changing model weights, so today it has become a basic skill for anyone who develops artificial intelligence applications.
Among the more advanced techniques, zero-shot promptinging (defining direct task without example), few-shot prompting (provide the pattern of the model by showing several examples) and chain-of-thought prompting (the model can increase accuracy by encouraging step-by-step execution). Prompt engineering is a much faster, cheap and accessible method compared to fine-tuning because it only optimizes input, without changing model weights, so today it has become a basic skill for anyone who develops artificial intelligence applications.