A practical framework for assessing Retrieval-Augmented Generation (RAG) systems, focusing on the quality of both retrieved information and generated responses. It highlights key evaluation areas such as relevance, accuracy, and faithfulness while recommending a mix of offline testing and real-world user feedback to ensure reliable and trustworthy system performance.

This guide outlines a step-by-step approach to creating a no-code chatbot powered by Retrieval-Augmented Generation (RAG) using AWS services. It demonstrates how to leverage tools like Amazon S3, Bedrock, and Lambda to build a chatbot that can retrieve and generate responses based on your own data. The process includes uploading datasets, setting up a knowledge base, tokenizing data, and deploying the chatbot—all without writing any code. This method enables rapid prototyping and validation of AI ideas, making it accessible for product managers and startup founders to test concepts efficiently

Natural Language Processing (NLP) is transforming healthcare by unlocking insights from unstructured data. Key applications include automating clinical documentation, improving medical coding, enhancing clinical decision support, accelerating drug discovery, driving patient engagement, and enabling proactive health monitoring. This leads to better efficiency, accuracy, and patient outcomes.