#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
#OXT/USDT analysis :
#OXT has broken out and retested a previously respected support zone. Price is expected to bounce back from this level and test previous highs. This indicates a potential continuation of the bullish trend, as the retest confirms the strength of the support.
TF : 1D
Entry : $0.1070
Target : $0.1570
SL : $0.0857
#OXT finally broken up the trading range on 8H Time frame,we will have good pump again after a pullback,let's see what will happen 💫
❄️@signals_bitcoin_crypto❄️
❄️@Shadow_support0o❄️
#OXT result
2 nd target achieved in just 3 days ✅✅
One more huge quick profit 15%🤑💰🤑
👉 Still thinking? The more you wait more you lose profit
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#OXT has good bullish momentum on Weekly time frame,we are waiting for a pullback and rebuy,buy dip
📈
❄️@signals_bitcoin_crypto❄️
❄️@Shadow_support0o❄️