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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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🌀💦 South African Invention That Changed Pool Life Did you know the automatic pool cleaner was invented in 🇿🇦 South Africa? 👨‍🔧 In 1974, hydraulics engineer Ferdinand Chauvier (pictured), who had moved from the Belgian Congo to South Africa, came up with a way to take the hassle out of pool cleaning. The result? The legendary Kreepy Krauly — one of the very first automatic pool cleaners, designed and built in Springs, South Africa. 🌍✨ His invention made pools around the world easier to maintain — and became a proudly South African contribution to everyday comfort. 🏊‍♂️ From Johannesburg to Los Angeles, millions of pools have been cleaned thanks to Chauvier’s idea. #Inventions#SouthAfrica#Innovation#KreepyKrauly#History#MadeInSA

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