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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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Venture Village Wall 🦄

@venturevillagewall · Post #4339 · 03/08/2025, 01:00 PM

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Top 5 Insights from Trendoscope Lab 1️⃣ a16z's new report reveals top B2C AI apps by traffic differ from revenue leaders like photo and video editors. Read more 2️⃣ Google launches AI Mode with Gemini, initially for Google One subscribers, enhancing AI SEO relevance. Details here 3️⃣ Short drama apps surge with 221% annual growth in downloads, affecting traditional streamers like Netflix and Disney+ that declined by 35%. Insight found here 4️⃣ Google and Meta enable developers to access cameras on XR devices, easing previous privacy restrictions. More info and here 5️⃣ Google enhances Android widget visibility with new filters and dedicated pages. Learn more #AI#Google#Tech#Streaming#XR#Investments#B2C#Apps#VideoEditing#Photos#Dramas#SEO#Privacy#Android#Widgets#Investing#VC#a16z#Trends#Innovation

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