RAG-ing Success: Guide to choose the right components for your RAG solution on AWS

With the rise of Generative AI, <a href="https://arxiv.org/pdf/2005.11401.pdf" rel="noopener ugc nofollow" target="_blank">Retrieval Augmented Generation(RAG)</a> has become a very popular approach for using the power of Large Language Models (LLMs). It simplifies the whole Generative AI approach while reducing the need to fine-tune or eventually train an LLM from scratch. Some of the reasons why RAG has become so popular are: <ul> <li>You can avoid <a href="https://en.wikipedia.org/wiki/Hallucination_(artificial_intelligence)" rel="noopener ugc nofollow" target="_blank">hallucinations</a> where the model tries to be “creative” and provides false information by making things up.</li> <li>You can always get the latest information/answer around a topic or question without worrying about when was the training cut off for the foundation model.</li> <li>You can avoid spending time, effort and money on complex process of fine tuning or eventually training on your data.</li> <li>Your architecture becomes loosely coupled.</li> </ul> Below diagram depicts a simplified component architecture diagram of RAG: <a href="https://medium.com/@pandey.vikesh/rag-ing-success-guide-to-choose-the-right-components-for-your-rag-solution-on-aws-223b9d4c7280">Learn More</a>