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1 OIT Artificial Intelligence (AI) Explorers Program Evaluating Retrieval-Augmented Generation (RAG) Systems: Best Practices, Challenges, and Evolving Approaches Authors: Artz, Matthew; Whittington, Michael; Rego, Sergio; Rutherford, Cody D. September 2024 Abstract This document is an overview of Retrieval-Augmented G... | Evaluating_Retrieval_Augmented_Generation_RAG_Systems.pdf |
2 Figure 1. RAG allows LLMs t o access an index of information, that it can reference when prompted, without needing to be trained on the data. The first step of the RAG system is to organize the user's input data in a way that the model can make use of it. This involves breaking the input into smaller, man ageable par... | Evaluating_Retrieval_Augmented_Generation_RAG_Systems.pdf |
3 Naïve RAG is one of the earliest methods, which follows a traditional framework commonly known as “Retrieve-Read”. This framework, described in more detail in the prior section, encompasses three primary phases of indexing, retrieval, and generation (Gao, et al. 2024). Figure 2. Framework of the Naïve RAG method (Gao... | Evaluating_Retrieval_Augmented_Generation_RAG_Systems.pdf |
4 Use Cases and Applications of RAG The table below highlights various use cases where RAG systems can be effectively applied, demonstrating their versatility in enhancing information retrieval, personalization, and content creation across different domains. Table 1. Example use cases of RAG and how they can be applied... | Evaluating_Retrieval_Augmented_Generation_RAG_Systems.pdf |
5 Challenges and Considerations in Evaluating RAG Systems The field of RAG is relatively new and lacks widely accepted standardized evaluation methodologies. The continuous evolution and increased complexity of RAG and generative AI technologies further complicates the evaluation process (Noblis 2023). Below, we will d... | Evaluating_Retrieval_Augmented_Generation_RAG_Systems.pdf |
6 Automated Approach: Traditional NLP Metrics Traditional Natural Language Processing (NLP) metrics remain foundational in evaluating RAG systems. These include BLEU, ROUGE, and METEOR, each offering unique insights into the quality of generated text. Human Evaluation Due to some of the challenges that automated LLM ev... | Evaluating_Retrieval_Augmented_Generation_RAG_Systems.pdf |
7 Establish clear evaluation goals: Setting explicit evaluation objectives is crucial for determining the effectiveness of a RAG model. Clear objectives help align the evaluation process with the intended use case, ensuring that the assessment focuses on the most relevant aspects of the model's performance. For example... | Evaluating_Retrieval_Augmented_Generation_RAG_Systems.pdf |
8 Communities of Practice: Engaging with the AI community at CMS, such as the #ai_community Slack channel, can provide valuable opportunities for sharing insights and emerging trends in RAG evaluation. By sharing these insights, it allows AI practitioners to collaborate on challenge s, exchange innovative approaches, a... | Evaluating_Retrieval_Augmented_Generation_RAG_Systems.pdf |
9 Scoring Tool Use Case Name Team Data Sources RAG Architecture Category Description Measure Weight (% of 100) Score (1-5) Comments Retrieval Evaluation Measures of how well the system retrieves contextually appropriate information that is aligned with the user's question. 16. 67 Generation Evaluation Measures whether ... | Evaluating_Retrieval_Augmented_Generation_RAG_Systems.pdf |
10 Case Study The AI Explorers team developed a chatbot that allows users to ask questions and receive answers from information in the “Medicare and You 2024” Handbook. The chatbot leverages an open-source LLM, Mistral 7B, with RAG to generate responses from the handbook information. To thoroughly assess this solution,... | Evaluating_Retrieval_Augmented_Generation_RAG_Systems.pdf |
11 Case Study-RAG System Scorecard Use Case Name Using Gen. AI to Create a 'Medicare and You 2024' handbook chatbot Team AI Explorers Data Sources 'Medicare and You 2024' handbook RAG Architecture LLM-Mistral-7B, RAG Framework-Llama Index, Eval Model-Tru Lens Category Description Measure Weight (% of 100) Score (1-5) C... | Evaluating_Retrieval_Augmented_Generation_RAG_Systems.pdf |
12 Conclusion RAG systems offer significant potential by combining the power of LLMs with timely, relevant data, addressing some limitations of static models. However, despite these advancements, RAG systems face challenges including hallucinations and biases inherent t o LLMs. Evaluating these systems helps foster res... | Evaluating_Retrieval_Augmented_Generation_RAG_Systems.pdf |
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