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| Format: | Artículo científico |
| Sprache: | en |
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Universidad Nacional de Colombia
2023
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| Online-Zugang: | https://www.redalyc.org/articulo.oa?id=49681157002 https://www.redalyc.org/journal/496/49681157002/ https://www.redalyc.org/journal/496/49681157002/html/ https://www.redalyc.org/journal/496/49681157002/49681157002.epub https://www.redalyc.org/journal/496/49681157002/movil |
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| _version_ | 1866815550008590336 |
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| author | Juan David Velásquez-Henao |
| author_facet | Juan David Velásquez-Henao |
| contents | Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering Juan David Velásquez-Henao Carlos Jaime Franco-Cardona Lorena Cadavid-Higuita Ingeniería ChatGPT prompt design prompt engineering large language models ChatGPT is a versatile conversational Artificial Intelligence model that responds to user input prompts, with applications in academia and various sectors. However, crafting effective prompts can be challenging, leading to potentially inaccurate or contextually inappropriate responses, emphasizing the importance of prompt engineering in achieving accurate outcomes across different domains. This study aims to address this void by introducing a methodology for optimizing interactions with Artificial Intelligence language models, like ChatGPT, through prompts in the field of engineering. The approach is called GPEI and relies on the latest advancements in this area; and consists of four steps: define the objective, design the prompt, evaluate the response, and iterate. Our proposal involves two key aspects: data inclusion in prompt design for engineering applications and the integration of Explainable Artificial Intelligence principles to assess responses, enhancing transparency. It combines insights from various methodologies to address issues like hallucinations, emphasizing iterative prompt refinement techniques like posing opposing questions and using specific patterns for improvement. This methodology could improve prompt precision and utility in engineering. 2023 artículo científico 0012-7353 https://www.redalyc.org/articulo.oa?id=49681157002 https://www.redalyc.org/journal/496/49681157002/ https://www.redalyc.org/journal/496/49681157002/html/ https://www.redalyc.org/journal/496/49681157002/49681157002.epub https://www.redalyc.org/journal/496/49681157002/movil 10.15446/dyna.v90n230.111700 en http://www.redalyc.org/revista.oa?id=496 Dyna application/pdf Universidad Nacional de Colombia Dyna (Colombia) Num.230 Vol.90 |
| format | Artículo científico |
| id | redalyc_49681157002 |
| language | en |
| publishDate | 2023 |
| publisher | Universidad Nacional de Colombia |
| spellingShingle | Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering Juan David Velásquez-Henao Ingeniería ChatGPT prompt design prompt engineering large language models Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering Juan David Velásquez-Henao Carlos Jaime Franco-Cardona Lorena Cadavid-Higuita Ingeniería ChatGPT prompt design prompt engineering large language models ChatGPT is a versatile conversational Artificial Intelligence model that responds to user input prompts, with applications in academia and various sectors. However, crafting effective prompts can be challenging, leading to potentially inaccurate or contextually inappropriate responses, emphasizing the importance of prompt engineering in achieving accurate outcomes across different domains. This study aims to address this void by introducing a methodology for optimizing interactions with Artificial Intelligence language models, like ChatGPT, through prompts in the field of engineering. The approach is called GPEI and relies on the latest advancements in this area; and consists of four steps: define the objective, design the prompt, evaluate the response, and iterate. Our proposal involves two key aspects: data inclusion in prompt design for engineering applications and the integration of Explainable Artificial Intelligence principles to assess responses, enhancing transparency. It combines insights from various methodologies to address issues like hallucinations, emphasizing iterative prompt refinement techniques like posing opposing questions and using specific patterns for improvement. This methodology could improve prompt precision and utility in engineering. 2023 artículo científico 0012-7353 https://www.redalyc.org/articulo.oa?id=49681157002 https://www.redalyc.org/journal/496/49681157002/ https://www.redalyc.org/journal/496/49681157002/html/ https://www.redalyc.org/journal/496/49681157002/49681157002.epub https://www.redalyc.org/journal/496/49681157002/movil 10.15446/dyna.v90n230.111700 en http://www.redalyc.org/revista.oa?id=496 Dyna application/pdf Universidad Nacional de Colombia Dyna (Colombia) Num.230 Vol.90 |
| title | Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering |
| topic | Ingeniería ChatGPT prompt design prompt engineering large language models |
| url | https://www.redalyc.org/articulo.oa?id=49681157002 https://www.redalyc.org/journal/496/49681157002/ https://www.redalyc.org/journal/496/49681157002/html/ https://www.redalyc.org/journal/496/49681157002/49681157002.epub https://www.redalyc.org/journal/496/49681157002/movil |