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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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@repo_science · Post #3567 · 09/06/2023, 07:13 PM

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#python#dataEngineering 📃 Amazon Web Services in Action Description Master essential best practices for deploying and managing applications on Amazon Web Services. This revised bestseller is packed with techniques for building highly available and scalable architectures and automating deployment with Infrastructure as Code. ⚖️1.7 GB 🔗Enlace ----- Main channel: @repo_science Coupons: @freecoupons_reposcience -----

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@repo_science · Post #3495 · 08/03/2023, 01:47 PM

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@repo_science · Post #3084 · 04/21/2023, 02:49 PM

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#apacheSpark#ETL#dataEngineering#Python 🎚 Apache Spark 3 for Data Engineering and Analytics with Python [Video] 🗣David Mngadi 🌐 Inglés ⚖️2.1GB 🔗Link ----- Canal principal:@repo_science Cupones: @freecoupons_reposcience -----

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