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michael-lewis.com | ||
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bdtechtalks.com
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| | | | | Retrieval augmented generation (RAG) enables you to use custom documents with LLMs to improve their precision. | |
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wandb.ai
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| | | | | [AI summary] This article explains Retrieval Augmented Generation (RAG), a technique that enhances AI models by integrating real-time data retrieval with generative models to improve accuracy, reduce hallucinations, and adaptability in dynamic fields like healthcare, finance, and customer support. | |
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www.jamesserra.com
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| | | | | [AI summary] The article provides an in-depth overview of OpenAI and Large Language Models (LLMs), discussing their architecture, applications, and integration into business processes. It also explores Microsoft's ecosystem of AI tools, including Azure OpenAI Studio, Azure AI Studio, and Microsoft Copilot Studio, highlighting their distinct roles in AI development and deployment. The piece emphasizes the importance of leveraging LLMs for tasks such as natural language processing, content generation, and data analysis, while also addressing the challenges and considerations in implementing these technologies. Additionally, it touches on the use of Retrieval-Augmented Generation (RAG) techniques to enhance the capabilities of LLMs by incorporating external dat... | |
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datadan.io
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| | | Linear regression and gradient descent are techniques that form the basis of many other, more complicated, ML/AI techniques (e.g., deep learning models). They are, thus, building blocks that all ML/AI engineers need to understand. | ||
