How to Scale RAG and Build More Accurate LLMs

RAG-enabled GenAI is a powerful approach for improving the accuracy of large language models by leveraging data streaming architectures with Confluent, Flink, and MongoDB. This approach allows data teams to contextualize prompts in real-time with domain-specific company data, making it more likely that the LLM will identify the right pattern in the data and provide a correct response. RAG enables fine-tuning of existing models without requiring significant expertise or resources, but must be implemented in a way that provides accurate and up-to-date information and is governed to scale across applications and teams. An event-driven architecture is beneficial for integrating disparate sources of data from across an enterprise in real-time, promoting reusability and allowing data augmentation for multiple LLM-enabled applications. This approach enables decentralized development teams to work separately to achieve performance and accuracy goals, decreasing time to market and increasing scalability.

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