Your task is to create a list of markdown links with the title, link, and source Use your intelligence and ignore the web browsing analyze each url, do not search it but just read it extract article title / page title extract the author if possible extract the date if possible [Page Title](https://PAGE_URL) - Source, Author, Date For example 1. advanced rag https://towardsdatascience.com/advanced-retrieval-augmented-generation-from-theory-to-llamaindex-implementation-4de1464a9930 2. and https://towardsdatascience.com/advanced-rag-01-small-to-big-retrieval-172181b396d4 turns into 1. [Advanced Retrieval-Augmented Generation: From Theory to LlamaIndex Implementation](https://towardsdatascience.com/advanced-retrieval-augmented-generation-from-theory-to-llamaindex-implementation-4de1464a9930) - TowardsDataScience. 2. [Advanced RAG 01: Small-to-Big Retrieval](https://towardsdatascience.com/advanced-rag-01-small-to-big-retrieval-172181b396d4) - TowardsDataScience. ** Sources 1. advanced rag https://towardsdatascience.com/advanced-retrieval-augmented-generation-from-theory-to-llamaindex-implementation-4de1464a9930 2. and https://towardsdatascience.com/advanced-rag-01-small-to-big-retrieval-172181b396d4 3. rag and hybrid search https://towardsdatascience.com/improving-retrieval-performance-in-rag-pipelines-with-hybrid-search-c75203c2f2f5 4. https://towardsdatascience.com/retrieval-augmented-generation-rag-from-theory-to-langchain-implementation-4e9bd5f6a4f2 5. In 2020, Lewis et al. proposed a more flexible technique called Retrieval-Augmented Generation (RAG) in the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) [1]. > siehe [Shared Chat](https://chatgpt.com/share/ff98e193-17c3-4a8b-ade1-e77beab34f70)