Large Language Models (LLMs), such as GPT4, leverage the power of vector embeddings and databases to address the challenges posed by evolving data. These embeddings, when combined with a vector database or search algorithm, offer a way for LLMs to gain access to an up-to-date and ever-expanding knowledge base. This ensures LLMs remain capable of generating accurate and contextually appropriate outputs, even in the face of constantly changing information. This approach is sometimes called Retrieval Augmented Generation (RAG).
This talk will introduce the topic of RAG and demonstrate the benefits of using Redis Enterprise as a vector database and Amazon Bedrock.
Principal Applied AI Engineer