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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 #3590 · 12/20/2024, 12:23 PM

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Google's Growth Dilemma Revealed Google faces a challenge with 5.5 billion users searching three times daily, leading to a near 98% penetration of its services. The company's pressure to show growth for investors may have led to risky strategies, including diversifying user journeys to boost commercial queries. A leaked document outlines these changes aimed at enhancing growth beyond organic metrics. Find out more in our podcast: Кухня. 📈 Listen here: Кухня Podcast For the full leaked document: Leaked Document #Google#Search#Users#Growth#Investment#Penetration#CommercialQueries#Tech#Strategy#Marketing#DocumentLeak#Podcast#DigitalMarketing#UserJourney#Innovation#Business#Alphabet#TechCulture#Earnings#MarketTrends