#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
#MASK/USDT analysis :
#MASK is currently in an uptrend, characterized by higher highs (HHs) and higher lows (HLs), and is trading above the 200-period exponential moving average (EMA).
The price is presently consolidating above a key support zone, and it is anticipated that it will bounce back from this level, continuing its upward trajectory and potentially testing previous highs.
TF : 4H
Entry : $2.877
Target : $3.300
SL : $2.610
#MASK/USDT analysis :
#MASK is in an uptrend. After breaking above the 200 EMA, the price is now sustaining above it. The price is currently consolidating over the support zone and is expected to bounce back from the current level, testing higher levels. Wait for a pullback before entering a long position.
TF : 4h
Entry : $2.265
Target : $2.531
SL : $2.129