Anatomy of a RAG System Architecture

For deploying a RAG System Architecture, consider that in a production environment requirements may vary when choosing a vector database, amount of data to be ingested, models used for creating embeddings, and architecture design when choosing a cloud platform. A RAG system can be built from scratch or implemented using solutions that already have the necessary components. Following best practices is also critical when designing the system, to avoid common issues like hallucinations, or data exposure. Also consider that the model can be changed through the time, and using layer architecture may be helpful for future changes or updates. What is a RAG System? Presenting false or inaccurate information when not knowing the response, using unvalidated sources, or giving outdated data are some of the challenges for LLMs. How to solve this, and improve the knowledge base? Retrieval-Augmented Generation Architecture (RAG) is the approach for solving this problem. RAG uses some methods, and to