This paper compares Retrieval-Augmented Generation (RAG) and fine-tuning as methods for incorporating domain-specific data into Large Language Models (LLMs). The study evaluates these approaches on multiple LLMs, including Llama2-13B, GPT-3.5, and GPT-4, using a dataset from the agriculture industry. The goal is to determine which method is more effective for generating accurate, context-specific knowledge for agricultural applications. The paper introduces a comprehensive pipeline for generating high-quality, industry-specific questions and answers. This pipeline includes data acquisition, information extraction from PDFs, question and answer generation, and fine-tuning of models. The study also evaluates the effectiveness of RAG and fine-tuning in capturing geographic-specific knowledge and improving the accuracy of answers. The results show that fine-tuning the model increases accuracy by over 6 percentage points, and combining it with RAG further increases accuracy by 5 percentage points. The fine-tuned model also demonstrates the ability to leverage information from across geographies to answer specific questions, increasing answer similarity from 47% to 72%. The study highlights the importance of domain-specific knowledge in agricultural applications and the potential of LLMs to be adapted to respond to industry-specific needs. The paper also discusses the trade-offs between RAG and fine-tuning, including the cost and complexity of fine-tuning, and the effectiveness of RAG in providing contextually relevant answers. The study concludes that RAG and fine-tuning can be used together to improve the performance of LLMs in agricultural applications, paving the way for further applications of LLMs in other industrial domains.This paper compares Retrieval-Augmented Generation (RAG) and fine-tuning as methods for incorporating domain-specific data into Large Language Models (LLMs). The study evaluates these approaches on multiple LLMs, including Llama2-13B, GPT-3.5, and GPT-4, using a dataset from the agriculture industry. The goal is to determine which method is more effective for generating accurate, context-specific knowledge for agricultural applications. The paper introduces a comprehensive pipeline for generating high-quality, industry-specific questions and answers. This pipeline includes data acquisition, information extraction from PDFs, question and answer generation, and fine-tuning of models. The study also evaluates the effectiveness of RAG and fine-tuning in capturing geographic-specific knowledge and improving the accuracy of answers. The results show that fine-tuning the model increases accuracy by over 6 percentage points, and combining it with RAG further increases accuracy by 5 percentage points. The fine-tuned model also demonstrates the ability to leverage information from across geographies to answer specific questions, increasing answer similarity from 47% to 72%. The study highlights the importance of domain-specific knowledge in agricultural applications and the potential of LLMs to be adapted to respond to industry-specific needs. The paper also discusses the trade-offs between RAG and fine-tuning, including the cost and complexity of fine-tuning, and the effectiveness of RAG in providing contextually relevant answers. The study concludes that RAG and fine-tuning can be used together to improve the performance of LLMs in agricultural applications, paving the way for further applications of LLMs in other industrial domains.