What is RAG?

RAG is a technique for augmenting LLM knowledge with additional data.

Here's how it works:

Retrieval: When you ask a question, the RAG system first retrieves relevant information from knowledge base. We generally use vector stores as knowledge bases. Vector stores are specialized databases designed to store and manage high-dimensional vectors representing textual or multimedia data. These are optimized for fast retrieval of similar vectors based on their numerical representations.
Augmentation: This retrieved information is then presented to the LLM, essentially augmenting its knowledge base for the specific conversation.
Generation: With access to both its internal knowledge and the retrieved data, the LLM can now generate a more accurate, up-to-date, and informative response to your query.

Overall, RAG is a powerful approach for building next-generation chatbots. It empowers them to be more helpful, reliable, and trustworthy companions in our digital interactions. For understanding it in detail, please refer to the video tutorial in the first slide of this project.

Project - Building a RAG Chatbot from Your Website Data using OpenAI, Langchain and Vector Database

What is RAG?

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