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  "body_markdown": "0\n0\nML Spring\nPosts\nRAGs-101: An introduction to llamaindex\nRAGs-101: An introduction to llamaindex\nDon't let your LLMs hallucinate! 🚀\nAkshay Pachaar\nNovember 04, 2023\nLLMs have taken the world by storm, they are powerful, versatile and have applications in a wide variety of fields.\nHowever, there's a big downside called hallucination. This basically means that LLMs can produce wrong outputs, make up facts, and their knowledge is limited to the data they were trained on.\nThis is where\nRetrieval Augmented Generation (RAGs)\nbecomes Important.\nIn simple words RAG is a technique to ground your LLMs to generate responses to your queries based on a custom knowledge-base that you provide.\nNow multiple question might arise:\nWhat is the meaning of custom knowledge base?\nWhat kind of data it can have?\nHow is this data stored and fed to an LLM?\nAnd what it takes to build such a system?\nSubscribe to keep reading\nThis content is free, but you must be subscribed to ML Spring to continue reading.\nAlready a subscriber?\nSign in\n.\nNot now\nReply\nNewest first\nAdd your comment\nLogin\nLogin\nor\nSubscribe\nto participate\n.",
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