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Source channel @githubtrending · Post #14993 · Jul 24

#jupyter_notebook Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability. https://github.com/langchain-ai/rag-from-scratch

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ChatGPT AI Technology News

@chatgpt_officialnews · Post #184 · 07/08/2025, 03:53 PM

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🧠AI Robot Chef Learns 200+ Recipes by Watching YouTube At the University of Cambridge, researchers trained a robot chef to learn over 200 cooking recipes—just by watching YouTube videos! 📸 ❓Here’s how it works: 👀 The robot watches cooking videos 🧠 Uses AI to identify ingredients, tools, and steps 🧑‍🍳 Replicates the recipes on its own in a real kitchen! Even better? It rated the dishes and improved its skills over time—just like a real chef! 👨‍🍳📈 🎯Why it matters: AI is evolving from text and speech to physical interaction—blending computer vision, robotics, and deep learning to understand and act in the real world. NeuralHubAI might not cook your dinner (yet)… but we’re already helping you build, think, and create faster every day. What will you teach AI next? ✨ ➖➖➖➖🔻 🧠 BOT: @Chatgpt_OfficialBOT 💎@Chatgpt_OfficialNews #️⃣#AIrobot#NeuralHubAI#FutureTech#CookingWithAI ➖➖➖➖🔺

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