Retrieval-Augmented Generation (RAG) Architecture: Cloud Cost Optimization
Retrieval-Augmented Generation (RAG) has emerged as one of the most effective architectures for building scalable software, where you can get accuracy and domain-specific generative AI systems. Therefore, you can combine a large language model (LLMs) with a retrieval mechanism that fetches relevant documents from an external knowledge base for your business platform. RAG allows organizations to improve response accuracy without the need for extensive fine-tuning. RAG also provides significant benefits in maintaining the adaptability and knowledge grounding for better implementation in cloud environments introduces unique cost considerations. Therefore, for many business owner can be an easy decision for their software, where you can update your account for compute, storage, networking, and database expenses, while also comparing RAG with alternative strategies such as fine-tuning. Well, in this blog, we will explore the cost implications of RAG in the cloud and provide strategies to op...