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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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AI & Law

@ai_and_law · Post #602 · 06/27/2025, 07:04 AM

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🇺🇸Microsoft Faces Watchdog Pushback on AI Advertising Microsoft’s ambitious marketing of its Copilot AI features has come under scrutiny. The National Advertising Division (NAD) has recommended that the company revise or discontinue certain productivity claims tied to Microsoft 365 Copilot, citing a lack of objective evidence. While Microsoft cited impressive user perception stats—like 70% reporting increased productivity—the NAD found the study insufficient to support hard ROI claims. More than just numbers, the watchdog also flagged the branding itself. With "Copilot" used across multiple tools, including Business Chat, users may not understand what the product can, and cannot, do. NAD advised Microsoft to make material limitations clear and avoid conflating distinct features under a single name. As AI tools enter mainstream business settings, regulatory clarity around marketing promises is becoming non-negotiable. #AI#MicrosoftCopilot#AIAdvertising#AITransparency

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