From the course: LLM Foundations: Vector Databases for Caching and Retrieval Augmented Generation (RAG)

Applications of RAG

From the course: LLM Foundations: Vector Databases for Caching and Retrieval Augmented Generation (RAG)

Applications of RAG

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What are some of the popular applications for retrieval-augmented generation today? RAG is revolutionizing the way several business processes are being done. It is helping in improving efficiency and reducing time to respond. First, there are interactive chatbots that businesses use to communicate with their customers. Chatbots are now more powerful and can use RAG to answer customer questions about products and help troubleshoot problems. RAG can help in automated responses to customer queries by email. Similar to chatbots, the responses can contain detailed information and answers to these queries. RAG can help with root cause analysis of technical issues faced. Based on log messages, absorbed metrics, and information from manuals, RAG can help predict potential root causes quickly and aid in resolution of such issues in a timely manner. On e-commerce websites, RAG can help customers quickly find what they are searching for and provide good narratives about the product or service. They can also customize such information for the customer. Enterprises have help desk for functions like human -- repeating. Enterprises have help desk for functions like human resources, legal, or logistics. These functions can be automated with RAG to help employees find quick answers to their questions and problems. Enterprises have large document hubs with several hundred documents, and searching for specific information across these documents is laborious. RAG can help build document search capabilities on these hubs, and provide quick pointers to these documents that are relevant to the user search. Having discussed the concepts of RAG, let's now implement a simple RAG system in the next chapter using Milvus.

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