LlamaIndex Sessions: 12 RAG Pain Points and Solutions

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  • เผยแพร่เมื่อ 23 ก.พ. 2024
  • We’re excited to feature Wenqi Glantz for a personal walkthrough video of her popular “12 RAG Pain Points and Solutions” blog post, which is the most comprehensive cheatsheet we’ve seen of pain points that occur at every stage of the RAG pipeline: data ingestion, retrieval, reranking, synthesis, and more.
    Source paper: towardsdatascience.com/12-rag...

ความคิดเห็น • 12

  • @kevon217
    @kevon217 4 หลายเดือนก่อน

    Excellent compilation. Super helpful.

  • @christyson4245
    @christyson4245 4 หลายเดือนก่อน

    This is brilliant! Thanks for sharing!

  • @DonBranson1
    @DonBranson1 4 หลายเดือนก่อน +1

    simply amazing. I liked your constructive response to a paper with a pessimistic title. Embrace your challenges and keep moving forward. Appreciate the mapping to LllamaIndex Notebooks.

  • @kenchang3456
    @kenchang3456 2 หลายเดือนก่อน

    Excellent video, and very informative. Thank you very much.

  • @duongkstn
    @duongkstn 4 หลายเดือนก่อน +1

    very informative, thanks !

  • @karanv293
    @karanv293 4 หลายเดือนก่อน +3

    im genuinely confused. what is the most accurate strategy to date?
    Theres so many tutorials out there , which one should I use for my use case. If you could guide me on that and then I can follow along a tutorial of somekind would be great.
    For example, i saw your posted workshop of advanced RAG with gemini.
    Should i follow that as the guide?

    • @evermorecurious91
      @evermorecurious91 4 หลายเดือนก่อน +3

      Haha. You can read the SOTA of RAG paper. This stuff keeps evolving, so you better follow one good source or be that source.

    • @karanv293
      @karanv293 4 หลายเดือนก่อน +1

      @@evermorecurious91 hahaha yeah I guess just stick with one and go with it.

  • @tecnopadre
    @tecnopadre 4 หลายเดือนก่อน +1

    Can be all this solutions implemented at once?

  • @sysadmin9396
    @sysadmin9396 4 หลายเดือนก่อน +1

    I have a python flask chatbot working hosted on AWS. Issue is all users that query the bot are getting answers from other users, in other words, it’s just one huge chat session for many users. How can I separate the sessions?