Generative AI is a major disruptor, but it's important to understand just how and why it works. In addition, you need to understand what happens to your data before you can put it to work.
Read this technical blog for a detailed dive into GenAI and large language models, and how Dell Technologies enables users to maintain data sovereignty and control while providing GenAI outcomes that meet your needs.
What is Retrieval Augmented Generation (RAG)?
Retrieval Augmented Generation (RAG) is a process used in large language model (LLM) applications to retrieve relevant knowledge base content, augment the user prompt with this domain-specific content, and then feed both the prompt and content into the LLM to generate a more complete and useful response.
How does RAG improve customer support interactions?
RAG improves customer support interactions by integrating legacy and future knowledge base articles into a help desk chatbot, allowing it to respond to questions with new information obtained from custom document datasets, thus providing more accurate and context-rich responses.
What are the benefits of using Llama2 with RAG?
The Llama2 model offers several benefits when used with RAG, including the ability to run locally on-premises, support for a variety of sizes, and performance that can match or exceed other models like ChatGPT, all while allowing full control over domain-specific content and ensuring data privacy.