#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
#FET/USDT Analysis-
The price has formed a symmetrical triangle pattern. Given the current bullish market scenario, there is a high probability of an upward breakout.
If the breakout occurs, the price is likely to rally toward the target zone from the breakout point.
T.F.- 1-D
ENTRY- as soon as it gives breakout
SL- 1.2
TARGET- 2.04
Note: If the stop-loss is triggered before entry, disregard the trade as the price action may develop differently.
#FET/USDT analysis :
#FET has broken down the 200 EMA and previous support levels. It is now undergoing a pullback and retesting the resistance zone. The price is expected to reject from there and bounce back to continue its bearish momentum.
TF : 1H
Entry : $1.170
Target : $1.057
SL : $1.252