#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
#COTI/USDT analysis :
#COTI is currently forming higher highs (HHs) and higher lows (HLs) along the trendline. The price is consolidating above this trendline, suggesting a potential bounce back and a test of higher levels.
TF : 1W
Entry : $0.10200
Target : $0.18200
SL : $0.07670
#COTI/USDT analysis :
#COTI is in an uptrend, forming a structure of higher highs (HHs) and higher lows (HLs). The price has broken out and successfully retested the trendline, continuing its bullish trajectory. Currently, the price is trading above a minor resistance zone, which has now become a buy zone. It is anticipated that the price will rise further and test higher levels.
TF : 1H
Entry : $0.14450
Target : $0.16145
SL : $0.13292