RAG explained in 6 mins | Retrieval Augmented Generation| AI

Hello everyone. Welcome to my channel. In this video, we are discussing one of the most important breakthroughs in generative AI, that is RAG, or retrieval augmented generation. If LLMs are the engine of AI, RAG is the fuel that combines information retrieval and text generation, which makes AI more accurate. So, without a further ado, let's dive into our video. So, what is RAG? RAG is a method which combines two components of an AI that are information retrieval, generative language models. In simple terms, the process looks First, the user questions. After that, there is retrieve relevant information. Next is provide retrieved context to LLM. And lastly, generate the accurate responses. Therefore, as the model uses the real information during the generation, the response become more reliable and accurate. How it got named RAG? The name retrieval augmented generation comes from two core steps involved in the process. Retrieval refers to searching a database source to find information that is relevant to the user's prompt. Augmented means enhancing the input prompt with retrieved information. And generation, it refers to the creating the final response. Hence, the model not only generate answers, but it also generate responses that are augmented with retrieved information. History of RAG. In 2020, researchers from Meta introduced the concept of RAG. Before RAG, AI systems mainly relied on two methods. The first is large pre-trained language models. These models generated text depending on the training data. The next is information retrieval systems. These systems search documents but did not generate natural language responses. Therefore, RAG combines the strength of both methods, which are the retrieval systems provide accurate information, and the language model generates human-like responses. Therefore, RAG has become one of the most important architectures today. The components of RAG. A RAG system has several components. Knowledge source, document chunking, embedding model, vector database, retriever, large language model or generator. So, these are the components of RAG. How RAG works. The working process of RAG can be explained step by step. The first is data collection. It is source collection of documents that the system will source. This may include websites, APIs, PDFs, database, research papers, and so on. Next is document chunking. Here, the large documents are divided into smaller and manageable pieces called chunks. It helps the system to process huge documents more efficiently. For example, the research paper is divided into paragraphs, then text chunks. The third step is embedding generation. In embedding, each text chunk is converted to a vector representation that capture the semantic meaning. Fourth step is vector storage. The embeddings are stored in a vector database, which allows fast similarity search. The step five is user query. When a user ask a question, the query is converted into an embedding. Step six is similarity search. The system compares the query embedding with the stored embeddings and retrieves the most relevant document chunks. [music] The step seven is context augmentation. The retrieved documents are added to the prompt that is sent to the language model. Step eight is response generation. Finally, the retrieved documents are sent to the language model such as GPT-4 or Llama. Model then generates the final response using this retrieved information. What problem does RAG solve? RAG solves some of the key problems such as hallucination. Sometimes, language model generate false information which looks like it is correct. RAG reduces hallucination as the model generates answers using the real data. Outdated information. Generally, language models cannot easily update their information after the training process. However, RAG allows the systems to use latest information without retraining the model. Data privacy. We cannot put our organization's secret documents into the public GPT training set. However, we can put it in a private RAG system. Hence, the RAG enables the models to answer questions based on the private company data. So, these are some of the problems RAG solves. Applications of RAG. RAG is widely used in so many areas such as customer support system, research assistance, enterprise knowledge assistance, health care systems, legal document analysis. So, these are the some of the applications of RAG. So now, let's summarize this video. RAG allows AI systems to generate responses that are more accurate and reliable by combining information retrieval with generative language models. This architecture helps overcome many limitations of traditional language models such as hallucinations, outdated information, and data privacy. As AI continues to evolve, RAG will play very important role in building a smart, trustworthy, and knowledge-driven AI applications. Okay, this is all for today's episode. I hope you enjoyed this video. If you do, do not forget to like, share, and subscribe my channel. Thank you for watching. See you in the next episode.