Building a Chatbot like ChatGPT

Chatbots powered by LLMs have demonstrated impressive fluency in conversational tasks like customer service. However, their lack of world knowledge limits their usefulness for domain-specific question answering. In this chapter, we explore how to overcome these limitations through Retrieval-Augmented Generation (RAG). RAG enhances chatbots by grounding their responses in external evidence sources, leading to more accurate and informative answers. This is achieved by retrieving relevant passages from corpora to condition the language model’s generation process. The key steps involve encoding corpora into vector embeddings to enable rapid semantic search and integrating retrieval results into the chatbot’s prompt.

We will also provide foundations for representing documents as vectors, indexing methods for efficient similarity lookups, and vector databases for managing embeddings. Building on these core techniques, we will demonstrate...