PROBES snapshot: An agricultural knowledge assistant using large language models

Patil, M D and Grecia, B and Daniel, J R (2025) PROBES snapshot: An agricultural knowledge assistant using large language models. Other. ICRISAT, Patancheru.

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Global Research Program - Resilient Farm and Food Systems

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UNSPECIFIED

Abstract

This one-page brief, developed by the Digital Transformation Accelerator (DTA) team at the Alliance of Bioversity International and CIAT, is based on the Agricultural Knowledge Assistant probe led by Patil Mukund, Senior Scientist - Soil Physics at ICRISAT. The probe explores how large language models (LLMs) can be used to help researchers and practitioners access, synthesize, and navigate large volumes of agricultural knowledge more efficiently. By enabling users to query documents, datasets, and technical resources in natural language, the tool aims to reduce time spent searching for information and improve evidence use in research and decision-making. Implemented as an early-stage, safe-to-fail experiment, the probe focuses on understanding the accuracy, usefulness, and limitations of LLM-based knowledge assistants in a scientific context. The brief highlights key insights to inform future development and responsible integration of generative AI tools within CGIAR research workflows.

Item Type:	Monograph (Other)
Divisions:	Global Research Program - Resilient Farm and Food Systems
CRP:	UNSPECIFIED
Series Name:	Probes
Uncontrolled Keywords:	agricultural research, artificial intelligence, knowledge management, information management, decision support systems
Subjects:	Others > Agricultural Research Others > Digital Agriculture
Depositing User:	Mr Nagaraju T
Date Deposited:	09 Feb 2026 08:27
Last Modified:	09 Feb 2026 08:27
URI:	http://oar.icrisat.org/id/eprint/13469
Links:	Google Scholar

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