Translations:FACTS About Building Retrieval Augmented Generation-based Chatbots/7/en

Chat-GPT’s release, the emergence of vector databases, and the widespread use of retrieval augmented generation (RAGs) (8) marked the beginning of a new era in the Chatbot domain. Now, LLMs can understand user intents with simple prompts in natural language, eliminating the need for complex intent variant training, synthesize enterprise content coherently, thereby empowering chatbots with conversational capability beyond scripted intent recognition. While LLMs bring their generative capabilities to construct coherent, factual, and logical responses to user queries, vector database-powered information retrieval (IR) systems augment LLMs ability to retrieve fresh content. Tools like LangChain (1) and Llamaindex (9) facilitate chatbot construction, and orchestration of complex workflows including memory, agents, prompt templates, and overall flow. Together, vector-search based IR systems, LLMs, and LangChain-like frameworks form core components of a RAG pipeline and are powering generative AI chatbots in the post Chat-GPT era.