  The integration of artificial intelligence into agricultural practices,
specifically through Consultation on Intelligent Agricultural Machinery
Management (CIAMM), has the potential to revolutionize efficiency and
sustainability in farming. This paper introduces a novel approach that
leverages large language models (LLMs), particularly GPT-4, combined with
multi-round prompt engineering to enhance decision-making processes in
agricultural machinery management. We systematically developed and refined
prompts to guide the LLMs in generating precise and contextually relevant
outputs. Our approach was evaluated using a manually curated dataset from
various online sources, and performance was assessed with accuracy and GPT-4
Scores. Comparative experiments were conducted using LLama-2-70B, ChatGPT, and
GPT-4 models, alongside baseline and state-of-the-art methods such as Chain of
Thought (CoT) and Thought of Thought (ThoT). The results demonstrate that our
method significantly outperforms these approaches, achieving higher accuracy
and relevance in generated responses. This paper highlights the potential of
advanced prompt engineering techniques in improving the robustness and
applicability of AI in agricultural contexts.
