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    Advanced RAG
    tylerTyler Rivera@tyler
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    Advanced RAG 10: Corrective Retrieval Augmented Generation (CRAG)
    Advanced RAG 10: Corrective Retrieval Augmented Generation (CRAG)Medium
    Intuitive Example, Priciples, Code Explanation and Insights about CRAG
    Implementing Advanced RAG in Langchain using RAPTOR
    Implementing Advanced RAG in Langchain using RAPTORMedium
    Usually in conventional RAG we often rely on retrieving short contiguous text chunks for retrieval. But when we are working with…
    Using Feedback to Improve Your Application: Self Learning GPTs
    Using Feedback to Improve Your Application: Self Learning GPTsLangChain Blog
    We built and hosted a simple demo app to show how applications can learn and improve from feedback over time. The app is called "Self Learning GPTs" and it uses LangSmith to gather feedback and then automatically use that feedback to improve over time. It does this by creating few-shot
    Multi-Vector Retriever for RAG on tables, text, and images
    Multi-Vector Retriever for RAG on tables, text, and imagesLangChain Blog
    Summary Seamless question-answering across diverse data types (images, text, tables) is one of the holy grails of RAG. We’re releasing three new cookbooks that showcase the multi-vector retriever for RAG on documents that contain a mixture of content types. These cookbooks as also present a few ideas for pairing
    Rerank Overview — Cohere
    Rerank Overview — CohereCohere
    How Rerank Works The Rerank API endpoint , powered by the Rerank models , is a simple and very powerful tool for semantic search. Given a query and a list of documents , Rerank indexes the documents from most to least semantically relevant to the query. Get Started Example with Texts In the example ...
    RAG But Better: Rerankers with Cohere AI
    RAG But Better: Rerankers with Cohere AIYouTube
    LangChain Multi-Query Retriever for RAG
    LangChain Multi-Query Retriever for RAGYouTube
    What We’ve Learned From A Year of Building with LLMs – Applied LLMs
    What We’ve Learned From A Year of Building with LLMs – Applied LLMsApplied LLMs
    A practical guide to building successful LLM products, covering the tactical, operational, and strategic.