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btw literally everybody is reporting that Agentic RAG is beating “trad RAG” by leaps and bounds its probably the #1 most cited result in RAG after “have you tried BM25” from Merrill Lutsky on the Raza Habib pod

30,551 Aufrufe • vor 1 Jahr •via X (Twitter)

11 Kommentare

Profilbild von Rahul Dave
Rahul Davevor 1 Jahr

@corbtt @MerrillLutsky @RazRazcle Add one more reporter. Financial documents.

Profilbild von swyx 🔜 @aiDotEngineer (Jun 3-5)
swyx 🔜 @aiDotEngineer (Jun 3-5)vor 1 Jahr

@corbtt @MerrillLutsky @RazRazcle wdym

Profilbild von Rainmaker
Rainmakervor 2 Jahren

Here I share an XGBoost model that delivers a 25% CAGR with minimal drawdown on Visa stock. In this free Substack post I share code and commentary for a powerful Machine Learning strategy that delivers powerful returns.

Profilbild von Janaka Abeywardhana
Janaka Abeywardhanavor 1 Jahr

@MerrillLutsky @RazRazcle Hold that orig tweet implies agentic RAG does use semantic search???

Profilbild von Jo Kristian Bergum
Jo Kristian Bergumvor 1 Jahr

@MerrillLutsky @RazRazcle I had a meeting this week with founders of an AI support bot that said the same thing

Profilbild von Sherwood 💬
Sherwood 💬vor 1 Jahr

@MerrillLutsky @RazRazcle A wild @MerrillLutsky appears

Profilbild von Ak
Akvor 1 Jahr

@MerrillLutsky @RazRazcle Building a hosted version of this at !

Profilbild von sorta_sota
sorta_sotavor 1 Jahr

@MerrillLutsky @RazRazcle how can I get snipd to export like that

Profilbild von Raza Habib
Raza Habibvor 1 Jahr

@MerrillLutsky The first team I saw do this was actually @bloopdotai almost 2 years ago now

Profilbild von swyx 🔜 @aiDotEngineer (Jun 3-5)
swyx 🔜 @aiDotEngineer (Jun 3-5)vor 1 Jahr

@MerrillLutsky @bloopdotai i wonder if theres a canonical paper bc mine is the jerry liu talk

Profilbild von Christian Nonis
Christian Nonisvor 1 Jahr

@MerrillLutsky @RazRazcle I am team graph rag

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Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by Zain, experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:

Andrew Ng

124,458 Aufrufe • vor 1 Jahr