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

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