Ai, & LLMs in Farming. What is Retrieval-Augmented Generation ?

Prompted and edited by Greg Walters, the Cornell University report, ​RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

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What You Will Know After Reading This Article:
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1. The Practical Applications of AI in Agriculture: Understand how RAG and fine-tuning methods are being applied to tackle real-world challenges in agriculture, offering farmers tailored advice and insights.
2. Comparison Between GPT-3.5 and GPT-4: Gain insight into the performance differences and unique strengths of GPT-3.5 and GPT-4 in agricultural contexts, helping to choose the right tool for specific farming needs.
3. The Future of Farming with AI: Learn about the potential future applications of AI in agriculture, from enhancing crop yield predictions to automating irrigation schedules, and how they can contribute to more sustainable farming practices.

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RAG VS FINE-TUNING: PIPELINES TRADEOFFS AND A CASE STUDY ON AGRICULTURE" presents a compelling argument for the integration of cutting-edge NLP technologies in agriculture.

​By carefully analyzing the advantages and limitations of RAG and Fine-Tuning, the paper charts a path forward for the development of AI-driven agricultural systems. ​

​These systems promise not only to enhance productivity and sustainability but also to empower farmers with the knowledge and insights needed to navigate the challenges of 21st-century farming.

RAG and Fine-Tuning are two approaches for adapting LLMs to specialized fields like agriculture. RAG combines the retrieval of relevant information with generative models to provide more accurate and contextually relevant responses. In contrast, Fine-Tuning involves adjusting an LLM's parameters on a specific dataset to tailor its outputs to particular needs or domains. The paper investigates these methods' trade-offs, considering factors like accuracy, resource requirements, and adaptability to dynamic agricultural data.

This study set out to evaluate how well large language models (LLMs) like LLama 2, GPT-3.5, and GPT-4 tackle complex agricultural issues, using Retrieval-Augmented Generation (RAG) and fine-tuning techniques.

It found that RAG excels in contexts where data is directly relevant, enhancing accuracy by utilizing data embeddings efficiently, albeit with potential verbosity in outputs. Fine-tuning, on the other hand, produces precise and concise responses, ideal for domain-specific learning like crop yield improvement or irrigation optimization, though it requires significant initial investment for model training.

​The process involved stages of data collection, analysis, and evaluation, concluding with insights into the transformative potential of LLMs in agriculture and beyond, emphasizing the need for industry-specific adaptations to maximize benefits.

​​Here's an executive summary outlining the high points:

1. Comparison of RAG and Fine-Tuning: The paper examines the benefits and drawbacks of using RAG and Fine-Tuning for incorporating proprietary and domain-specific data into LLMs, using agriculture as a case study.
   
2. Pipeline Proposal: A detailed pipeline for fine-tuning and RAG is proposed, highlighting the stages of data acquisition, PDF information extraction, question and answer generation, and evaluation, aimed at generating location-specific insights for farmers.
   
3. Performance Metrics: Metrics to assess the performance of RAG and Fine-Tuning stages are introduced, including accuracy improvements and the effectiveness of dataset generation in capturing geographic-specific knowledge.
   
4. Agricultural Dataset Study: An in-depth study on an agricultural dataset shows how LLMs can provide location-specific insights, demonstrating a significant increase in accuracy through fine-tuning and RAG.
   
5. Implications for AI in Agriculture: The findings highlight the potential of LLMs in revolutionizing agriculture by providing precise, geography-specific answers to farmers, suggesting a broader application of LLMs in various industries.
   

A key takeaway is the delineation of performance baselines for LLMs using either RAG or fine-tuning, illustrating the trade-offs between accuracy, verbosity, and cost. The study not only compares these methodologies but also pioneers a pipeline for applying them across industries, with a primary focus on agriculture, showing how they can create more efficient models from question and answer generation to tailored industry solutions.

Moreover, the research demonstrates an effective strategy for generating industry-specific Q&A pairs through structured document understanding and model collaboration. This approach optimizes token usage and suggests a potential for mixing models or methods for different components of the Q&A process. It introduces metrics to evaluate the quality of questions and answers, underscoring the importance of continuous refinement in understanding LLM capabilities, especially the balance between the benefits and costs of RAG and fine-tuning.

​Ultimately, while both RAG and fine-tuning offer distinct advantages, their applicability varies by the specific needs, data size, and available resources. This study lays the groundwork for future exploration into combining these methods and improving dataset generation for targeted LLM applications, hinting at the potential for multi-modal fine-tuning that incorporates structured information from diverse sources like PDFs and images.

​What is RAG?  What is Retrieval-Augmented Generation (RAG) ? refers to an approach that combines the retrieval of relevant information from a dataset with the capabilities of generative language models. This method enhances the model's ability to provide accurate and contextually appropriate responses by first fetching related documents or data and then generating answers based on this retrieved information. RAG is particularly valuable in specialized fields like agriculture, where it can leverage domain-specific knowledge to offer precise, tailored advice.

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Findings

​​The study presents three main findings regarding the application of Retrieval-Augmented Generation (RAG) and Fine-Tuning methodologies in agriculture:

Effectiveness of RAG and Fine-Tuning: The research demonstrates that both RAG and Fine-Tuning significantly enhance the model's ability to provide accurate and contextually relevant answers to agricultural queries. RAG, by retrieving relevant documents before generating responses, and Fine-Tuning, by adjusting the model specifically to agricultural data, each improve the model's performance in unique ways.

​We employ Retrieval-Augmented Generation (RAG) (Lewis et al., 2020), which is an innovative approach that combines the power of retrieval and generation mechanisms, to create high-quality answers. R

​Superiority in Providing Specific Insights: The study finds that the combination of RAG with Fine-Tuning offers superior results in generating precise, actionable insights for farmers. This hybrid approach leverages the strengths of both techniques, leading to more relevant and detailed advice, particularly for location-specific agricultural issues.

Potential for Transformative Impact in Agriculture: The findings highlight the transformative potential of integrating advanced NLP techniques into the agricultural sector. The use of RAG and Fine-Tuning methodologies can revolutionize how information is delivered and utilized in agriculture, offering a pathway towards more informed decision-making and sustainable practices.​

​Examples:
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The report provides real-world examples to illustrate the effectiveness of Retrieval-Augmented Generation (RAG) and Fine-Tuning in the context of agricultural questions. One notable example detailed the response differences to a question regarding the main concern of blowing dust from excessively tilled fallow fields in the low precipitation wheat production region of the Inland Pacific Northwest. The reference answer focused on soil loss and air quality.

• Vicuna + RAG provided a succinct response, highlighting soil erosion and loss of topsoil as main concerns, along with potential health risks from blowing dust containing pollutants.
• Llama2 13B + RAG emphasized the negative impact on air quality due to dust inhalation, without directly addressing soil loss.
• GPT-4's response was comprehensive, covering a wide range of concerns including air quality, human health, crop yields, soil erosion, and ecosystem health. It discussed the negative impacts of excessive tillage leading to soil vulnerability, dust generation, and the broader environmental and health implications.
• GPT-4 + RAG offered a clear and concise answer, directly addressing the query by linking soil losses to reduced air quality and health risks from inhaling dust.
• GPT-4 fine-tuned + RAG provided a detailed answer addressing both soil loss and air quality, adding context about soil characteristics and emissions, highlighting the importance of sustainable farming practices to mitigate these issues.

​These examples underscore the nuanced capabilities of different models and configurations to generate relevant, accurate, and context-aware responses. They demonstrate the potential of RAG and Fine-Tuning in generating insightful answers tailored to specific agricultural inquiries, showcasing their applicability in real-world scenarios where precision and depth of understanding are crucial.

Wrap-Up

​The study's exploration into the capabilities of GPT-3.5 and GPT-4, through the lens of Retrieval-Augmented Generation (RAG) and fine-tuning techniques, marks a significant advancement in the application of AI within the agricultural sector.

The findings underscore the potential of these models to revolutionize agricultural practices, offering precision, efficiency, and sustainability. By enhancing decision-making with accurate, context-specific insights, these AI methodologies promise to support farmers in optimizing crop yields, managing resources more effectively, and navigating the complexities of modern farming with greater ease.

​Furthermore, the research opens up new avenues for AI-driven innovations in agriculture, from automated irrigation systems and pest management to crop health monitoring and yield prediction, paving the way for a future where technology and farming work hand in hand to address the pressing challenges of food security and environmental sustainability.

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Example Experiment: Answer Correctness 

Directly from the study, an experiment regarding 'Correctness'. 

Example of a partially correct answer.

Correctness

Score: Partially Correct
Question: What are some factors to consider when focusing on soil health testing in vineyards?
Answer: Based on the context information, some factors to consider when focusing on soil health testing in vineyards include:

•  Consistency with the timing of sampling and the soil testing lab.
•  Understanding the interpretation of the measured value from the lab.
•  Focusing testing on functions you want to improve.
•  Keeping good records of lab results and vineyard management for long-term soil health tracking.Not expecting changes to happen immediately.
•  Assessing block variability for effective management.

 Having fun exploring soil through a new lens. It’s important to note that these factors are not mutually exclusive and may overlap or interact with each other in different ways.  Additionally, the specific factors that are most important may vary depending on the specific vineyard and its management goals.

Reference Answer: Some factors to consider when focusing on soil health testing in vineyards include the functions you want your soil to serve (e.g., supplying plant nutrients, reducing erosion, or increasing aggregate stability), the cost of soil health testing, the frequency of sampling for different soil properties, and the time it takes to see measurable differences after a change in management.

Explanation: The answer mentions several factors to consider when focusing on soil health testing in vineyards, such as consistency with the timing of sampling, understanding the interpretation of the measured value, focusing on functions to improve, keeping good records, not expecting immediate changes, assessing block variability, and having fun exploring soil. Although it does not directly mention the cost of testing and the frequency of sampling, it covers other relevant factors and provides a comprehensive response.

Although the answer contains many factors related to soil health testing, it does not mention the cost of testing, an important factor. See report details, here.

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What is ​Retrieval-Augmented Generation ?

Retrieval-Augmented Generation (RAG) is an advanced technique that combines the retrieval of relevant information from a large dataset with the capabilities of a generative model to produce answers. Essentially, when a query is made, RAG first searches a database or corpus of documents to find the most relevant information related to the query. Then, it uses this retrieved information as context or input for a generative model, such as GPT (Generative Pre-trained Transformer), to generate a response that is informed by the specific data it found.
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This approach enables the model to produce more accurate, detailed, and contextually relevant answers than it could by relying on its pre-trained knowledge alone. RAG is particularly useful in scenarios where a model needs to provide up-to-date information or when the answer depends on specific data that may not be included in the model's initial training set. It bridges the gap between vast, static knowledge bases and the dynamic, generative power of language models, making it a powerful tool for a wide range of applications, from question-answering systems and chatbots to research assistance and content creation.

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​RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture

Microsoft
​Angels Balaguer, Vinamra Benara, Renato Luiz de Freitas Cunha, Roberto de M. Estevão Filho, Todd Hendry, Daniel Holstein, Jennifer Marsman, Nick Mecklenburg, Sara Malvar, Leonardo O. Nunes, Rafael Padilha, Morris Sharp, Bruno Silva, Swati Sharma, Vijay Aski, Ranveer Chandra

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​LinkedIn Post Intro🚀 Exciting advancements in AI are reshaping the future of agriculture! We've delved into the latest innovations from GPT-3.5 and GPT-4, revealing how these technologies are not just theoretical marvels but practical tools for today's farmers. Dive into our latest article to see how AI is making precision farming more accessible and efficient. 🌾🤖

​What You Will Know After Reading This Article:

1. The Practical Applications of AI in Agriculture: Understand how RAG and fine-tuning methods are being applied to tackle real-world challenges in agriculture, offering farmers tailored advice and insights.
2. Comparison Between GPT-3.5 and GPT-4: Gain insight into the performance differences and unique strengths of GPT-3.5 and GPT-4 in agricultural contexts, helping to choose the right tool for specific farming needs.
3. The Future of Farming with AI: Learn about the potential future applications of AI in agriculture, from enhancing crop yield predictions to automating irrigation schedules, and how they can contribute to more sustainable farming practices.