#jupyter_notebook#ai#llm#llms#multi_modal#openai#python#rag
Retrieval-Augmented Generation (RAG) is a technique that helps improve the accuracy of large language models by fetching relevant information from databases or documents. This approach ensures that the model's responses are based on up-to-date and accurate data, reducing errors and "hallucinations" where the model might provide false information. For users, RAG offers more reliable and trustworthy responses, allowing them to verify the sources used to generate those responses. This method also saves resources by avoiding the need to retrain models with new data.
https://github.com/FareedKhan-dev/all-rag-techniques
🌍 Some biodiversity hotspots, like the Cape Floristic Region in South Africa, pack over 9,000 plant species into just a tiny area—nearly 70% found nowhere else on Earth. ✨
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🌍 The Sundaland region in Southeast Asia is a biodiversity hotspot where over 15,000 plant species are found, nearly half of which exist nowhere else on Earth. ✨
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🌍 The Western Ghats in India hold more amphibian species than all of Europe, packed into a narrow mountain strip. This biodiversity hotspot shelters ancient frogs found nowhere else on Earth. ✨
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🌍 New species are still being discovered in biodiversity hotspots like the Eastern Himalayas, where over 350 new plants and animals have been found in just two decades. ✨
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