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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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#ONT/USDT analysis :
#ONT has successfully broken out and is currently testing a previously respected resistance zone. The price has rebounded from this zone, and it is expected to resume its upward momentum, aiming to test previous highs.
TF : 1D
Entry : $0.2595
Target : $0.3900
SL : $0.2050
#ONT/USDT analysis -
#ONT be in downtrend trading below 200ema. After a corrective pullback, price retraced to 200ema and getting rejected. It is expected to drop from current level and test previous lows.
TF : 4H
Entry : $0.2013
Target : $0.1597
SL : $0.2125