Figure RAG Architecture
RAG is basically designed to leverage LLMs on your own content or data. It involves retrieving relevant content to augment the context or insights as part of the generation process. However, RAG is an evolving technology with both strengths and limitations. a dedicated, custom, and accurate knowledge base, reducing the risk of LLMs offering general or non-relevant responses. For example, when the knowledge base is tailored to a specific domain (e.g., legal documents for a law firm), RAG equips the LLM with relevant information and terminology, improving the context and accuracy of its responses.
At the same time, there are limitations associated with RAG. RAG heavily relies on the quality, accuracy, and comprehensiveness of the information stored within the knowledge base. Incomplete, inaccurate or canada whatsapp number data missing information or data can lead to misleading or irrelevant retrieved data. Overall, the success of RAG hinges on quality data.
So, how are RAG models implemented? RAG has basically two key components: a retriever model and a generator model. The retriever model identifies relevant documents from a large knowledge corpus that are most likely to contain information pertinent to a given query or prompt. From this corpus, vectors (or embeddings) are generated that capture the semantic meaning of the content for a coherent and contextually accurate response. While there are multiple commercial and open-source RAG platforms in the market (LangChain, Llamaindex, Azure AI Search, Amazon Kendra, Abacus AI, and more), a typical implementation of the RAG models has five key phases.