#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
#OP/USDT analysis :
#OP is currently forming a continuation pattern within a downtrend. The price has broken previous lows, and it is anticipated that it will continue its bearish momentum. It is advisable to wait for a pullback to the identified zone for a short entry, as the previous swing low is expected to be tested.
TF : 1D
Entry : $1.493
Target : $1.076
SL : $1.773
#OP/USDT analysis :
#OP has broken out above the 200 EMA and the previous resistance zone with strong bullish momentum. It is now retesting the breakout zone. The price is expected to continue its bullish bias and test previous highs. Wait for a break of the $1.517 level to go long.
TF : 2H
Entry : $1.517
Target : $1.643
SL : $1.444