#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
#SHIB/USDT analysis :
The price of #SHIB has recently broken out of its trendline, indicating a potential bullish momentum. While a retracement to the 200 EMA is expected, the overall sentiment suggests that the price is likely to move upwards and test the swing high level.
TF : 4h
Entry : $0.00001480
Target : $0.00001734
SL : $0.00001313
#SHIB/USDT analysis :
#SHIB has successfully broken out of the trendline, demonstrating a strong bullish movement. It is anticipated that this upward momentum will continue, allowing the price to test higher levels.
TF : 1D
Entry : $0.00002536
Target : $0.00004567
SL : $0.00001827
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