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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

How RAG Gen AI Helps Businesses Retrieval-Augmented Generation (RAG) enhances AI by combining real-time data retrieval with generative models, making it highly effective for businesses. Key Benefits: Better Decision-Making – Provides real-time ins

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 eve

Most people think of RAG (Retrieval-Augmented Generation) as a text-only thing. But when we apply it to images, it unlocks serious potential — especially in safety, retail, and surveillance. I recently explored Vision-RAG using Weaviate + LangChain

In the ever-evolving AI landscape, a new player is making waves — Deepseek. While OpenAI, Google DeepMind, and Meta AI have been dominant forces, Deepseek is emerging as a formidable contender in the AI race.The recent buzz around Deepseek stems from

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

6 Chunking Methods for RAG you should know. I’ve explained it in a simple, step by step way. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴? 1. Chunking means splitting large documents into smaller pieces. 2. Helps LLMs search and understand data better. 3. Essent

6 Chunking Methods for RAG you should know. I’ve explained it in a simple, step by step way. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴? 1. Chunking means splitting large documents into smaller pieces. 2. Helps LLMs search and understand data better. 3. Essent