Retrieval augmented generation (RAG) is a technique for improving the accuracy of large language models (LLM) by combining information retrieval from proprietary data sources with text generation.

This session explores how to implement the RAG architecture, a powerful framework for the RAG technique that integrates the strengths of open LLMs—such as Llama 3—and vector databases to improve contextual relevance and accuracy of information retrieval.

This session shows how to:

• Use open source models and tools to create a robust retrieval system that can efficiently process and generate natural language responses.
• Set up a vector database to efficiently store and retrieve embeddings.
• Integrate RAG systems into existing infrastructure, including the hardware and software requirements for deploying these advanced models.
• Apply best practices for fine-tuning and customizing RAG models and vector databases for specific industry use cases through demos of practical applications.

Skill level: Intermediate

Session demos are done on the Intel® Tiber™ Developer Cloud,¹ a managed cloud environment for development efficiency, cost savings, and faster time to market.

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