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 applications to retrieve relevant knowledge base content, augment the user prompt with this domain-specific information, and then feed both the prompt and content into the model to generate a more complete and useful response.
How does RAG improve customer support interactions?
RAG improves customer support interactions by allowing chatbots to respond with accurate, human-like answers based on specific knowledge from custom document datasets, such as PDF files. This enables quicker response times and better self-help pathways for users.
What are the benefits of using Llama2 for RAG?
The Llama2 model provides several benefits for RAG, including the ability to run locally on-premises, a variety of sizes for different use cases, and a large context window that allows users to input extensive text or documents, facilitating the generation of detailed responses from new information.
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