#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
#BAKE/USDT analysis :
#BAKE is currently retracing towards the 200 EMA. The price is expected to retest this moving average before continuing its bearish momentum. The current level presents a favorable opportunity for a long entry.
TF : 2h
Entry : $0.1507
Target : $0.1700
SL : $0.1414
#BAKE/USDT analysis :
The price is in an uptrend, forming higher highs (HHs) and higher lows (HLs) above the 200-period exponential moving average (200 EMA). The price is expected to bounce back from this level and continue its bullish momentum, aiming to test the previous swing high.
TF : 1h
Entry : $0.2783
Target : $0.2929
SL : $0.2705