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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
🌎 Southern China’s Stone Forest, or Shilin, is a dramatic landscape of towering limestone pillars shaped by both natural erosion and centuries of quarrying. Human mining over the last thousand years has altered parts of this ancient karst region, creating new rock formations and open spaces alongside original spires. ✨
#geology⚡#humanimpact⚡#landscapes
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🌍 Cities often create "urban heat islands," where temperatures are higher than surrounding areas due to concrete, asphalt, and less greenery. This effect can raise cities’ temperatures by several degrees. ✨
#urbanization⚡#temperature⚡#humanimpact⚡#geography⚡#nature⚡#earth
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🌎 One of Earth's largest artificial lakes, Lake Volta in Ghana, was created in the 1960s by damming the Volta River. Spanning about 8,500 square kilometers, it supports fisheries and transportation but submerged forests and displaced over 78,000 people during its construction. ✨
#humanimpact⚡#reservoirs⚡#Africa
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🌎 The Florida Everglades, once a vast slow-moving river system, has been dramatically altered by canals, levees, and drainage for agriculture and cities. Over 50% of the original wetlands have been lost since the early 20th century, reducing habitat for species like the Florida panther and snail kite. ✨
#Everglades⚡#wetlands⚡#humanimpact
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