Building a retrieval-augmented generation (RAG) application requires seamless integration of diverse data sources—like internal documents, databases, and emails—with large language models (LLMs). The challenge lies in efficiently connecting, transforming, and orchestrating this data to provide context-rich, accurate responses.
In this Builder's Hub article, you'll learn how to implement RAG in your own AI workflows. This piece details how RAG addresses the shortcomings of LLMs, explains the role of vector stores, and discusses use cases for building RAG applications with Celigo's iPaaS like some of Celigo's own internal knowledge bots.