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Source channel @githubtrending · Post #14826 · Jun 12

#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

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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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