How to Evaluate and Improve RAG Applications for Safe Production Deployment

Retrieval-augmented generation (RAG) combines information retrieval systems with large language models (LLMs) to make AI-generated text more accurate and reliable by accessing the latest, relevant data. Developing RAG systems involves challenges such as selecting appropriate data sources, optimizing retrieval algorithms, ensuring seamless communication between LLM and retrieval components, and addressing security, safety, and compliance concerns. Evaluating RAG systems thoroughly is crucial before transitioning them to production, assessing performance, accuracy, and robustness under various scenarios. Tools like LangKit and WhyLabs AI Control Center play a vital role in this process, allowing developers to monitor and measure each step of development and make data-driven improvements.