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Prompt engineering for text generation: Difference between revisions
Prompt engineering for text generation (view source)
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{{see also|Prompt engineering|Prompts|Prompt engineering for image generation}} | {{see also|Prompt engineering|Prompts|Prompt engineering for image generation}} | ||
==Text-to-text models== | ==Text-to-text models== | ||
Prompt engineering is not limited to [[text-to-image generation]] and has found a fitting application in [[AI-generated art]]. Various [[templates]] and "[[recipes]]" have been created to optimize the process of providing the most effective textual inputs to the model. OpenAI has published such "recipes" for their language model that can be adapted to different downstream tasks, including [[grammar correction]], [[text summarization]], [[answering questions]], [[generating product names]], and functioning as a [[chatbot]]. <ref name="”2”">Oppenlaender, J (2022). A Taxonomy of Prompt Modifiers for Text-To-Image Generation. arXiv:2204.13988v2</ref> | [[Prompt engineering]] is not limited to [[text-to-image generation]] and has found a fitting application in [[AI-generated art]]. Various [[templates]] and "[[recipes]]" have been created to optimize the process of providing the most effective textual inputs to the model. OpenAI has published such "recipes" for their language model that can be adapted to different downstream tasks, including [[grammar correction]], [[text summarization]], [[answering questions]], [[generating product names]], and functioning as a [[chatbot]]. <ref name="”2”">Oppenlaender, J (2022). A Taxonomy of Prompt Modifiers for Text-To-Image Generation. arXiv:2204.13988v2</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 skill, and produce many iterations. <ref name="”9”">Shynkarenka, V (2020). Hacking Hacker News frontpage with GPT-3. Vasili Shunkarenka. 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 skill, and produce many iterations. <ref name="”9”">Shynkarenka, V (2020). Hacking Hacker News frontpage with GPT-3. Vasili Shunkarenka. https://vasilishynkarenka.com/gpt-3/</ref> | ||