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r/Rag is a subreddit with 21k members. Its distinguishing qualities are that the community is large in size, and has high activity.
Welcome to r/Rag, the community for everything Retrieval-Augmented Generation (RAG)! RAG combines retrieval systems with generative models to create more accurate responses, enhancing applications like customer support and research. Join us to discuss RAG techniques, projects, and tools. Whether you're a researcher, developer, or AI enthusiast, you'll find tips, tutorials, and support to innovate with RAG!
Popular Themes in r/Rag
#1
Solution Requests
: "Advanced Chunking/Retrieving Strategies for Legal Documents"
12 posts
#2
Advice Requests
: "New book suggestion- Unlocking Data with Generative AI and RAG"
7 posts
#3
Self-Promotion
: "I got tired of setting up APIs just to test RAG pipelines, so I built this"
4 posts
#4
Pain & Anger
: "RAG Pain points"
2 posts
Popular Topics in r/Rag
#1
Rag
: "Me when someone asks me "why bother with Rag when I can dump a pdf in chatGPT?""
209 posts
#2
Ai
: "Building Document search for RAG, for 2000+ documents. These documents are technical in nature, contAins tables , need suggestion!"
55 posts
#3
: "Best Pdf parser for academic papers"
27 posts
#4
Chatbot
: "DeepSeek's: Boost Your RAG Chatbot: Hybrid Retrieval (BM25 + FAISS) + Neural Reranking + HyDe"
26 posts
#5
Gpt
: "We’re Bryan Chappell (CEO) & Alex Boquist (CTO), Co-founders of ScoutOS—an AI platform for building and deploying your Gpt and AI solutions. AMA!"
25 posts
#6
Llm
: "Are Llms a total replacement for traditional OCR models?"
24 posts
#7
Context
19 posts
#8
Data
: "How people prepare Data for RAG applications"
15 posts
#9
Search
: "Building Document Search for RAG, for 2000+ documents. These documents are technical in nature, contains tables , need suggestion!"
12 posts
#10
Document
: "Advanced Chunking/Retrieving Strategies for Legal Documents"
10 posts
Member Growth in r/Rag
Yearly
+18k members(526.4%)
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