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==Prompt engineering== | ==Prompt engineering== | ||
[[Prompt engineering]] or [[Prompt design]] is the practice of discovering the prompt that gets the best result from the [[AI system]]. <ref name="”4”"></ref> The development of prompts requires human intuition with results that can look arbitrary. <ref name="”9”">Pavlichenko, N, Zhdanov, F and Ustalov, D (2022). Best prompts for text-to-image models and how to find them. arXiv:2209.11711v2</ref> Manual prompt engineering is laborious, it may be infeasible in some situations, and the prompt results may vary between various model versions. <ref name="”3”"></ref> However, there have been developments in automated [[prompt generation]] which rephrases the input, making it more model-friendly. <ref name="”5”"></ref> | [[Prompt engineering]] or [[Prompt design]] is the practice of discovering the prompt that gets the best result from the [[AI system]]. <ref name="”4”"></ref> The development of prompts requires human intuition with results that can look arbitrary. <ref name="”9”">Pavlichenko, N, Zhdanov, F and Ustalov, D (2022). Best prompts for text-to-image models and how to find them. arXiv:2209.11711v2</ref> Manual prompt engineering is laborious, it may be infeasible in some situations, and the prompt results may vary between various model versions. <ref name="”3”"></ref> However, there have been developments in automated [[prompt generation]] which rephrases the input, making it more model-friendly. <ref name="”5”"></ref> | ||
===Language models=== | ===Language models=== | ||
In [[language models]] like [[GPT]], the output quality is influenced by a combination of [[prompt design]], [[sample data]], and [[temperature]] (a [[parameter]] that controls the “creativity” of the responses). Furthermore, to properly design a prompt the user has to have a good understanding of the problem, good grammar skills, and produce many iterations. <ref name="”10”">Shynkarenka, V (2020). Hacking Hacker News frontpage with GPT-3. Vasili Shynkarenka. https://vasilishynkarenka.com/gpt-3/ </ref> | In [[language models]] like [[GPT]], the output quality is influenced by a combination of [[prompt design]], [[sample data]], and [[temperature]] (a [[parameter]] that controls the “creativity” of the responses). Furthermore, to properly design a prompt the user has to have a good understanding of the problem, good grammar skills, and produce many iterations. <ref name="”10”">Shynkarenka, V (2020). Hacking Hacker News frontpage with GPT-3. Vasili Shynkarenka. https://vasilishynkarenka.com/gpt-3/ </ref> | ||