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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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#FLOW/USDT analysis :
#FLOW has broken out above the 200 EMA and has retraced back to it. The price is currently in a correction phase, consolidating sideways over the support zone. The price has rebounded and broken out of the trendline. Now, the price is expected to resume its bullish momentum and test higher levels.
TF : 1D
Entry : $0.798
Target : $1.271
SL : $0.620