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dc.contributor.authorFONTENELLE, M. R.
dc.contributor.authorAMORIM, J. R. A. de
dc.contributor.authorCRUZ, M. A. S.
dc.contributor.authorLIMA, C. E. P.
dc.date.accessioned2025-10-09T18:48:36Z-
dc.date.available2025-10-09T18:48:36Z-
dc.date.created2025-10-09
dc.date.issued2025
dc.identifier.citationRevista DCS, v. 22, n. 83, p. 1-17, 2025.
dc.identifier.issn2224-4131
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1179549-
dc.descriptionThe climate emergency demands innovative methodological approaches to reach socioeconomically and environmentally vulnerable populations and areas as quickly as possible. This article proposes the use of Open Science, Prompt Engineering, and Prompt Chaining as tools for mapping future climate scenarios using automated methods produced with the aid of Generative Artificial Intelligence (AI – LLMs models). Based on an applied study, it demonstrates how these techniques can be integrated into climate mapping models focused on family farming, in line with IPCC frameworks on climate justice and just transition, significantly increasing productivity, scalability and reducing time and potential errors. The results indicate that AI, when guided by well-structured prompts and chained logical flows, can democratize access to complex analyses, support public policies, and strengthen the resilience of vulnerable communities. The workflow proposed here can be replicated for other areas, models, climate scenarios, and agricultural crops. The deposit of all Python scripts generated on the Zenodo open access platform is in line with the FAIR principles of open science
dc.language.isopor
dc.rightsopenAccess
dc.subjectObjetivos de Desenvolvimento Sustentável (ODS)
dc.subjectEngenharia de Prompt
dc.subjectInteligência Artificial Generativa
dc.subjectModelos de Linguagem de Grande Escala (LLMs)
dc.subjectMapeamento Climático Automatizado
dc.subjectCenários Climáticos Futuros
dc.subjectCiência Aberta
dc.subjectGeoprocessamento automatizado
dc.titlePrompt engineering and prompt chaining in artificial intelligence: tools for mapping future climate scenarios as mechanisms of adaptation and climate justice and just transition promotion.
dc.typeArtigo de periódico
dc.subject.thesagroMudança Climática
dc.subject.thesagroAgricultura Familiar
riaa.ainfo.id1179549
riaa.ainfo.lastupdate2025-10-09
dc.identifier.doi10.54899/dcs.v22i83.3456
dc.contributor.institutionMARIANA RODRIGUES FONTENELLE, CNPH; JULIO ROBERTO ARAUJO DE AMORIM, CPATC; MARCOS AURÉLIO SOARES CRUZ; CARLOS EDUARDO PACHECO LIMA, CNPH.
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