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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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Crypto M - Crypto News

@CryptoM · Post #65256 · 04/12/2026, 10:58 AM

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🚀 RAVE Token Movement Sparks Interest Among Investors Analyst @ai_9684xtpa posted on X about a notable transaction involving the RAVE token. An investor, identified as @EnHeng456 enai.bnb, purchased 46,336.76 RAVE tokens at an average price of $0.4556 on the second day of its launch. The tokens were held until 48 days ago, when they were transferred to Aster at a price of $0.39 per token, with the purpose of the transfer remaining unclear. Recently, 41,025 tokens were moved from Aster, making the investor one of the top nine holders on the BSC network. If the tokens had not been sold, the investor's return rate would have reached 462%, with an unrealized profit of $86,000. #RAVE#TokenMovement#Investors#BSC#Transaction#Aster#Crypto#Blockchain#Investment#ReturnRate#UnrealizedProfit#X

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