Introducing the RAG Evaluation White Paper and Scorecard

As CMS continues to explore artificial intelligence (AI), Retrieval-Augmented Generation (RAG) has emerged as a tool for enhancing the accuracy and contextual awareness of Large Language Models (LLMs) in applications like chatbots. Importantly, RAG enables these AI models to access and utilize up-to-date information by automatically retrieving and incorporating relevant content from agency documentation without requiring expensive training.

While this capability is powerful, RAG is not a silver bullet for ensuring accurate and reliable chatbot responses. Even with relevant content retrieved, LLMs can still produce hallucinations, or fabricated content, and inaccurate outputs, making robust evaluation crucial. As CMS explores RAG solutions, it is essential to systematically assess their performance, reliability, and adherence to ethical principles. 

Accordingly, this article introduces OIT's latest white paper on RAG Evaluation, which provides an overview of RAG, use cases, and applications. It also presents the RAG System Scorecard, a comprehensive evaluation framework for RAG applications.

What is RAG Evaluation?

RAG evaluation is an AI systems assessment approach that can be used to measure the effectiveness and efficiency of RAG-based systems. AI Explorer’s new RAG evaluation white paper and scorecard help ensure that CMS RAG applications maintain accuracy, relevance, and trustworthiness. The evaluation process covers three critical components of a RAG-based system:

• Retrieval: The process by which the system searches through the uploaded data to find information that is contextually relevant to the user's query.
• Augmentation: Refers to how the system enriches the user’s input prompt with relevant information retrieved in the previous step.
• Generation: Uses the retrieved and augmented information to produce a coherent and contextually appropriate response to the user’s input prompt.

With an understanding of what RAG evaluation entails, let's shift our focus to its significance for OIT in ensuring the effectiveness of RAG-based systems.

Significance for OIT

The RAG evaluation white paper and scorecard guides teams in conducting thorough evaluations through various approaches, including automated metrics and human judgment. This structured tool helps teams assess performance across multiple dimensions, including adherence to CMS's Responsible AI principles.

By implementing systematic evaluation practices, OIT teams can:

• Ensure RAG systems deliver accurate and relevant information.
• Identify and address potential issues early in development.
• Build trust in AI-powered solutions.
• Create more reliable and effective tools for CMS stakeholders.

Steps for Getting Started

1. Review the RAG evaluation white paper and scorecard.
2. Consider both automated and human evaluation methods for your specific needs.
3. Align evaluation metrics with your use case.
4. Engage with the CMS AI community through the #ai_community Slack channel.

Looking Forward

The RAG evaluation white paper and scorecard builds on CMS's commitment to responsible AI development, complementing existing resources like the CMS AI Playbook. By adopting systematic evaluation practices, we can create more trustworthy and effective AI solutions that better serve our mission.

Together, we're building a future where AI enhances our ability to provide quality healthcare services while maintaining the highest standards of reliability and responsibility.