#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
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#FLUX/USDT analysis :
#FLUX is currently in a downtrend, forming lower lows (LLs) and lower highs (LHs) below the 200 EMA. The price is rejecting from the resistance zone and is expected to decline further, maintaining its bearish momentum and potentially testing lower levels. Wait for pullback for short entry.
TF : 4h
Entry : $0.5124
Target : $0.4521
SL : $0.5531
#FLUX/USDT analysis :
#FLUX has recently rebounded from the support zone. It is anticipated to keep rising and test the resistance zone on the lower time frame (LTF).
TF : 1h
Entry : $0.5469
Target : $0.5847
SL : $0.5207