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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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EdgeMarket.AI 📣

@edgemarketai · Post #8128 · 04/24/2026, 07:54 AM

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VISION11

@vision11official · Post #1199 · 04/17/2023, 12:30 PM

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EdgeMarket.AI 📣

@edgemarketai · Post #8143 · 04/29/2026, 09:42 AM

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djangoproject

@djangoproject · Post #447 · 09/18/2017, 10:35 AM

https://pybee.org/news/buzz/2017-google-summer-of-code-final-report-dayanne-fernandes/ After almost 4 months of work on Google Summer of Code 2017, finally I'm completing my proposal. Every widget migration and every commit/PR/issue/discussion with my mentors about Cricket , Toga and rubicon-objc were detailed on the Issue 58. "Eating your own dog food" The best way to show that a product is reliable to the customers is use it. So, the way to show that #Toga is an effective tool to build a #GUI is to build a complete application using it. #Cricket is a graphical tool that helps you run your #test suites. Its current version is implemented using #Tkinter as the main GUI framework. So, why not test Toga inside of another product from #BeeWare? That's what I have acomplished during my GSoC work.

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