The text provided discusses the topic of Retrieval Augmented Generation (RAG) and its application in question answering using Large Language Models (LLMs). It covers various aspects such as chunking text, querying, context building, re-ranking, evaluation, and addressing hallucinations in generated text. The author also highlights the relevance of RAG in the context of advanced NLP techniques and its potential for future developments. Overall, the text serves as an insightful overview of RAG and its practical considerations.
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