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Hey I am on Medial • 2m

Hello Friends, I’ve been diving deep into Retrieval-Augmented Generation (RAG) and its evolved form — Agentic RAG — lately, and I’m genuinely curious about how others are thinking about it. I’d love to hear your ideas, interesting use cases, or even the challenges you foresee with applying RAG and Agentic RAG in enterprise or industry-specific contexts. Where do you see it being most useful? Are there areas where a more autonomous, tool-using Agentic RAG approach could better address existing pain points than traditional RAG? Drop your thoughts, pet projects, or even bold ideas here — it would be great to learn from this community

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🚨 Everyone says "RAG is dead" — but I say: It’s just been badly implemented. I’ve worked on AI systems where Retrieval-Augmented Generation (RAG) either changed the game… or completely flopped. Here’s the hard truth 👇 --- 🤯 Most teams mess up

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Hey I am on Medial • 3m

Retrieval-Augmented Generation (RAG) is a GenAI framework that enhances large language models (LLMs) by incorporating information from external knowledge bases, improving accuracy, relevance, and reliability of generated responses. Here's a more det

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Hey I am on Medial • 4m

I am trying to research on ways to make ar glasses more accessible or in other words, make the ar (augmented reality) glasses more affordable. Where do you think i should focus?

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Meta has released Llama 3.1, the first frontier-level open source AI model, with features such as expanded context length to 128K, support for eight languages, and the introduction of Llama 3.1 405B. The model offers flexibility and control, enabli