🚀 Turbocharge Your AI: Automating RAG Deployment on AWS with Terraform
In the fast-paced world of Generative-AI, Retrieval-Augmented Generation (RAG) has emerged as one of the leading approaches to enhance the capability of large language models. By combining the power of generative AI with the ability to retrieve and incorporate relevant information from external knowledge bases, RAG systems offer more accurate, contextually relevant, and up-to-date responses.
Even though there are Large Language Models (LLMs) with expansive context windows, RAG remains a more cost-effective option to maintain and supercharge AI capabilities. This approach allows organizations to leverage the power of AI without the hefty computational and financial costs associated with training and running massive models.
However, deploying and managing RAG systems at scale can be challenging. Enter the dynamic duo of AWS serverless technologies and Terrafrom — a combination that can streamline your deployment process and supercharge your AI infrastructure.
In this comprehensive guide, we’ll explore how to deploy an RAG system using AWS lambda and API gateway, all orchestrated by the infrastructure-as-code magic of Terrfrom. Let’s dive into the world of automated, scalable AI deployment.
