#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
#AUCTION/USDT analysis :
#AUCTION has tested the 200 EMA and rebounded from it. The price has broken out of the trendline, confirming the continuation of the bullish trend. The price is anticipated to test the swing high level.
TF : 30min
Entry : $33.35
Target : $37.30
SL : $30.98
#AUCTION/USDT analysis :
#AUCTION is in an uptrend, forming higher highs (HHs) and higher lows (HLs). The price has recently broken out of the trendline, indicating a potential resumption of its bullish momentum. It is anticipated that the price will continue to rise and test previous highs.
TF : 30min
Entry : $20.05
Target : $21.53
SL : $19.06