r/Rag is a subreddit with 74k members. The most common kinds of discussions are advice requests and solution requests, and the community frequently discusses rag, chatbot, pdf, ai, and pipeline, and they frequently recommend/review database, front end, and llm.
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
Advice Requests
: "Guidance for customer facing QnA RAG system: storing question embeddings"
6 posts
#2
Solution Requests
: "Need advice on digitizing hospital paper records into a RAG system (first large-scale project)"
3 posts
Popular Topics in r/Rag
#1
Rag
15 posts
#2
Chatbot
3 posts
#3
3 posts
#4
Ai
2 posts
#5
Pipeline
2 posts
#6
Tutorial
1 post
#7
System
1 post
#8
Conversion
1 post
#9
Dataset
1 post
#10
Search
1 post
Products Discussed in r/Rag
Flair Used in r/Rag
#1
Discussion
: "How would you design an enterprise-grade RAG system beyond the traditional “vector DB + LLM” approach?"
63 posts
#2
Showcase
: "fine-tuned a VLM for messy-PDF extraction, 46% → 91.1% on OmniDocBench. runs fully on your own hardware, looking for people to break it"
16 posts
#3
Tools & Resources
: "How to run llms and rags locally"
11 posts
#4
Tutorial
: "Built a local RAG app that answers questions from your own PDFs, fully offline"
4 posts
Member Growth in r/Rag
Yearly
+43k members(143.9%)
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Last updated: July 12, 2026