Exploring advanced system design – RAG and LangChain

Retrieval-Augmented Generation (RAG) is a development framework designed for seamless interaction with LLMs. LLMs, by virtue of their generalist nature, are capable of performing a vast array of tasks competently. However, their generality often precludes them from delivering detailed, nuanced responses to queries that necessitate specialized knowledge or in-depth expertise in a domain. For instance, if you aspire to use an LLM to address queries concerning a specific discipline, such as law or medicine, it might satisfactorily answer general queries but fail to respond accurately to those needing detailed insights or up-to-date knowledge.

RAG designs offer a comprehensive solution to the limitations typically encountered in LLM processing. In a RAG framework, the text corpus undergoes initial preprocessing, where it’s segmented into summaries or distinct chunks and then embedded within a vector space. When a query...