#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
#IOST/USDT analysis :
#IOST has tested the support zone and has shown a bounce back. It is advisable to wait for a price retracement to enter long positions. The price is expected to maintain its bullish momentum, with potential to reach higher levels.
TF : 4H
Entry : $0.008550
Target : $0.011323
SL : $0.007050
#IOST/USDT analysis :
#IOST is currently experiencing a decline from the resistance zone following rejections in that region. It is anticipated to maintain its downward momentum and possibly test previous lows.
TF : 2H
Entry : $0.00500
Target : $0.00427
SL : $0.00542
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