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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 #207 · 07/28/2025, 08:43 PM

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🚨Major Image Update Unlocked!🚨 Our AI image generation just got faster, smarter, and more customizable! ✏️ ➕ We’ve added powerful new models and settings to give YOU more control and stunning results: 🆕 FLUX.1 [schnell] – Ultra-fast with customizable settings 🆕 Stable Diffusion – Balanced, sharp, and detailed visuals ✅ LCM (Latent Consistency Model) – Lightning-fast and lightweight 🔍Why it matters: 🎯 Generate images in under 5 seconds 🪙 Uses fewer tokens – perfect for free users 🖼 Great for artistic, stylized outputs 🔄 Ideal for rapid experimentation and high-volume image creation ➡️Whether you're building, designing, or dreaming — our new models are here to help you create faster and better than ever. Try it now and unleash your imagination! ✨ ➖➖➖➖🔻 🧠 BOT: @Chatgpt_OfficialBOT 💎@Chatgpt_OfficialNews #️⃣#AIUpdate#LCM#StableDiffusion#FLUX#NeuralHubAI#AIArt ➖➖➖➖🔺

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