How to Train a Chatbot Using RAG and Custom Data

Retrieval-Augmented Generation (RAG) optimizes large language models by training them on specific, smaller knowledge bases. This process can reduce errors and hallucinations common in models trained on vast datasets. Using tools like LlamaIndex, developers can create custom RAG systems by uploading targeted data, such as Wikipedia pages for a US state tour guide chatbot. The process involves creating an index, configuring an embedding model, and querying the customized database. RAG allows for more accurate and context-specific responses in specialized applications.