#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
#ROSE/USDT analysis :
#ROSE is in an uptrend, forming higher highs and higher lows above the 200ema. The price is expected to sustain its bullish momentum and test new highs. Wait for a price retracement over the support zone for a long entry.
TF : 2h
Entry : $0.0606
Target : $0.0696
SL : $0.0561
#ROSE +%33 in just 5 days 🤑🤑
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