#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
#FTM/USDT analysis :
#FTM is currently in a bullish trend, characterized by higher highs and higher lows, with consistent support along the trendline. The price is expected to rebound from its current level and test previous highs. A breakout above the $0.8800 level would present an optimal entry point for further upward movement.
TF : 1D
Entry : $0.8800
Target : $1.4000
SL : $0.6550
#FTM/USDT analysis :
#FTM is currently consolidating sideways within the support and resistance zone. The price is bouncing back from the support zone and is anticipated to sustain its momentum and test the previous swing high within the resistance zone.
TF : 30min
Entry : $0.4193
Target : $0.4314
SL : $0.4127
#FTM +%70 🤑🤑
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