#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
#STRAX/USDT analysis :
#STRAX is currently in an uptrend, forming higher highs (HHs) and higher lows (HLs). The price is currently in a correction phase, retracing to test previously respected support zone. It is advisable to wait for the price to retest this zone for a long entry, as higher targets are likely to be achieved.
TF : 1D
Entry : $0.06163
Target : $0.09200
SL : $0.05174
#STRAX/USDT analysis :
#STRAX is presently in an uptrend, creating higher highs (HHs) and higher lows (HLs) above the 200 Exponential Moving Average (200EMA). The price is expected to continue its bullish momentum and soon test previous swing high.
TF : 30min
Entry : $0.05323
Target : $0.05666
SL : $0.05125
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