Towards Inclusive, Contextual, and Balanced Agricultural Advisories: Refining GPT-5 Agricultural Advisories for Kenya with Reinforcement Learning from Human Feedback
This study presents a human-in-the-loop framework to enhance the accuracy, inclusivity, and contextual relevance of GPT-5-based agricultural advisories in Kenya. Using over 2,800 real farmer queries from the iShamba SMS platform, researchers applied prompt optimization, expert review, and Reinforcem...
| Autores principales: | , , |
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| Formato: | Artículo preliminar |
| Lenguaje: | Inglés |
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Accelerating Impacts of CGIAR Climate Research for Africa
2025
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| Acceso en línea: | https://hdl.handle.net/10568/177324 |
| _version_ | 1855516185797328896 |
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| author | Nganga, Kevin Gitau Grossi, Amanda Wanjau, Agnes Njambi |
| author_browse | Grossi, Amanda Nganga, Kevin Gitau Wanjau, Agnes Njambi |
| author_facet | Nganga, Kevin Gitau Grossi, Amanda Wanjau, Agnes Njambi |
| author_sort | Nganga, Kevin Gitau |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This study presents a human-in-the-loop framework to enhance the accuracy, inclusivity, and contextual relevance of GPT-5-based agricultural advisories in Kenya. Using over 2,800 real farmer queries from the iShamba SMS platform, researchers applied prompt optimization, expert review, and Reinforcement Learning from Human Feedback (RLHF) to refine AI responses. The refined model achieved a 27% increase in satisfactory answers and a technical accuracy score of 1.95/2, outperforming baseline systems. Six key bias types—gender, social, regional, commercial, and linguistic—were identified and mitigated through localized data and bilingual support. The paper demonstrates how RLHF and participatory design can align generative AI with smallholder farmers’ needs, producing advice that is scientifically sound, equitable, and culturally grounded. Findings underscore the potential of inclusive AI frameworks to democratize climate-smart agricultural knowledge while safeguarding against bias in digital extension services |
| format | Artículo preliminar |
| id | CGSpace177324 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Accelerating Impacts of CGIAR Climate Research for Africa |
| publisherStr | Accelerating Impacts of CGIAR Climate Research for Africa |
| record_format | dspace |
| spelling | CGSpace1773242025-11-11T17:09:54Z Towards Inclusive, Contextual, and Balanced Agricultural Advisories: Refining GPT-5 Agricultural Advisories for Kenya with Reinforcement Learning from Human Feedback Nganga, Kevin Gitau Grossi, Amanda Wanjau, Agnes Njambi smallholders-smallholder farmers gender climate-smart agriculture-climate smart agriculture digital agriculture gender equality resilience artificial intelligence climate services-climate information services climate extension agricultural extension This study presents a human-in-the-loop framework to enhance the accuracy, inclusivity, and contextual relevance of GPT-5-based agricultural advisories in Kenya. Using over 2,800 real farmer queries from the iShamba SMS platform, researchers applied prompt optimization, expert review, and Reinforcement Learning from Human Feedback (RLHF) to refine AI responses. The refined model achieved a 27% increase in satisfactory answers and a technical accuracy score of 1.95/2, outperforming baseline systems. Six key bias types—gender, social, regional, commercial, and linguistic—were identified and mitigated through localized data and bilingual support. The paper demonstrates how RLHF and participatory design can align generative AI with smallholder farmers’ needs, producing advice that is scientifically sound, equitable, and culturally grounded. Findings underscore the potential of inclusive AI frameworks to democratize climate-smart agricultural knowledge while safeguarding against bias in digital extension services 2025-09-25 2025-10-24T14:44:03Z 2025-10-24T14:44:03Z Working Paper https://hdl.handle.net/10568/177324 en Open Access application/pdf Accelerating Impacts of CGIAR Climate Research for Africa Nganga K, Grossi A, Wanjau A. 2025. Towards Inclusive, Contextual, and Balanced Agricultural Advisories: Refining GPT-5 Agricultural Advisories for Kenya with Reinforcement Learning from Human Feedback. AICCRA Working Paper. Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA) |
| spellingShingle | smallholders-smallholder farmers gender climate-smart agriculture-climate smart agriculture digital agriculture gender equality resilience artificial intelligence climate services-climate information services climate extension agricultural extension Nganga, Kevin Gitau Grossi, Amanda Wanjau, Agnes Njambi Towards Inclusive, Contextual, and Balanced Agricultural Advisories: Refining GPT-5 Agricultural Advisories for Kenya with Reinforcement Learning from Human Feedback |
| title | Towards Inclusive, Contextual, and Balanced Agricultural Advisories: Refining GPT-5 Agricultural Advisories for Kenya with Reinforcement Learning from Human Feedback |
| title_full | Towards Inclusive, Contextual, and Balanced Agricultural Advisories: Refining GPT-5 Agricultural Advisories for Kenya with Reinforcement Learning from Human Feedback |
| title_fullStr | Towards Inclusive, Contextual, and Balanced Agricultural Advisories: Refining GPT-5 Agricultural Advisories for Kenya with Reinforcement Learning from Human Feedback |
| title_full_unstemmed | Towards Inclusive, Contextual, and Balanced Agricultural Advisories: Refining GPT-5 Agricultural Advisories for Kenya with Reinforcement Learning from Human Feedback |
| title_short | Towards Inclusive, Contextual, and Balanced Agricultural Advisories: Refining GPT-5 Agricultural Advisories for Kenya with Reinforcement Learning from Human Feedback |
| title_sort | towards inclusive contextual and balanced agricultural advisories refining gpt 5 agricultural advisories for kenya with reinforcement learning from human feedback |
| topic | smallholders-smallholder farmers gender climate-smart agriculture-climate smart agriculture digital agriculture gender equality resilience artificial intelligence climate services-climate information services climate extension agricultural extension |
| url | https://hdl.handle.net/10568/177324 |
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