#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
#ELF/USDT analysis :
#ELF is currently in an uptrend, forming higher highs (HHs) and higher lows (HLs). Following an impulsive move, the price has retraced to the 200 EMA and a key support zone. It is anticipated that the price will rebound from this level and resume its bullish momentum, aiming to test the previous swing high.
TF : 1D
Entry : $0.4500
Target : $0.8400
SL : $0.3550
#ELF/USDT analysis :
#ELF is currently consolidating above the previously respected support zone. The price is expected to bounce back from this level and continue its bullish rally, potentially testing previous highs.
TF : 1W
Entry : $0.4124
Target : $0.7853
SL : $0.3060