#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
#POLYX/USDT analysis :
#POLYX has rebounded from the previously respected support zone and is currently trading above a minor support level. The price is expected to move upward from this level and test the swing high resistance.
TF : 2h
Entry : $0.1790
Target : $0.2036
SL : $0.1688
#POLYX/USDT analysis :
#POLYX has broken out and retested the support zone above the 200 EMA. The price is expected to continue its bullish momentum and test previous highs.
TF : 15min
Entry : $0.2353
Target : $0.2593
SL : $0.2232
#POLYX/USDT analysis :
#POLYX is presently establishing a bullish channel. The price has recently rebounded from the support zone and is now progressing to test the previous swing high.
TF : 4H
Entry : $0.2231
Target : $0.2538
SL : $0.2029