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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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Architectural Shovel / Любовь Дмитриева

@arch_shovel · Post #663 · 09/07/2022, 08:31 PM

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C U R V E S / Under Magnitude by Marc Fornes / THEVERYMANY The strength of #undermagnitude is achieved by 'Intensive Curvature,' which is the maximization of double #curvature across the project while constraining maximum radii. The result is a #structure that has much tighter curvature with constant change of direction, and results in more structurally performance. 'Intensive Curvature' leads to the curly, tubular branching characteristics consistent across the studio's body of work. In order to achieve structural #stability , each stripe assumes high degrees of curvature individually and high degrees of double curvature in accumulation -- amounting to extreme structural #rigidity throughout the project. #arch_shovel#archdaily

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