AgriLLM How CGIAR Is Developing an AI-powered Agricultural Advisory Service for Global South
- Author: Digital Transformation Accelerator
- Full Title: AgriLLM: How CGIAR Is Developing an AI-powered Agricultural Advisory Service for Global South
- Type:
- Tags: #ai #localai #agriculture
- URL: https://www.cgiar.org/news-events/news/agrillm-how-cgiar-is-developing-an-ai-powered-agricultural-advisory-service-for-global-south
Highlights
- Unlike general-purpose AI tools, AgriLLM is rooted in scientific rigor, grounded in CGIAR knowledge and local realities, and tailored for people who rarely feature in the AI revolution: those who grow, raise, and fish for the food that feeds the world. (View Highlight)
- CGIAR’s Digital Transformation Accelerator (DTA) convened two workshops at ILRI, Nairobi that aimed to gather a foundational dataset of high-quality question and answer (Q&A) pairs that reflect the concerns, language, and context of key user personas – smallholder farmers seeking localized, practical advice; extension agents needing field-ready guidance; researchers looking for synthesis and analysis support; and policymakers requiring concise, data-driven insights. (View Highlight)
- Participants generated the Q&A pairs in a structured three-phase process. First, they generated broad agricultural topics relevant to various user personas. Then, they created realistic, needs-based questions from the perspectives of those personas, such as farmers, extension agents, and policy makers. Finally, teams collaborated to create clear, evidence-based answers for each question, including citations when possible. (View Highlight)
- Yassin outlined the planned follow-up activities from the workshops as follows:
• Collect additional Q&A pairs from CGIAR, and initiate Q&A pairs collection with FAO.
• Finalize post- processing and curation of the human-generated Q&A pairs; expand and diversify the training / test sets (e.g., by adding typos to the questions on purpose); and proceed to a second round of fine tuning.
• Deploy the fine- tuned model on the AI Assistant; start developing context-aware retrieval capabilities (location-specific, crop-specific); and develop onboarding flow to extract information about the user (View Highlight)