Studying Retrieval Augmented Era
The Structure, Operational Layers, and Greatest Practices for Efficient RAG Implementation
It might not come as a shock that retrieval augmented technology (RAG) is among the many most utilized methods on the planet of generative AI and huge language model-powered purposes. In actual fact, in accordance with a Databricks report, greater than 60% of LLM-powered purposes use RAG in some type. Subsequently, within the international LLM market, which is presently valued at round $6 Billion and rising at nearly 40% YoY, RAG undoubtedly turns into a kind of essential methods to grasp.
Constructing a PoC RAG pipeline shouldn’t be too difficult as we speak. There are available examples of code leveraging frameworks like LangChain or LlamaIndex and no-code/low-code platforms like RAGArch, HelloRAG, and so on.
A production-grade RAG system, however, consists of a number of specialised layers…