#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
#RAD/USDT analysis :
#RAD is currently experiencing a bearish trend, trading below the 200 EMA. The price is forming lower lows (LLs) and lower highs (LHs).
At present, it is testing the resistance zone along with the 200 EMA. A reversal is anticipated from this level, allowing the price to resume its bearish momentum and potentially test lower levels.
TF : 1H
Entry : $0.840
Target : $0.779
SL : $0.875
#RAD/USDT analysis :
#RAD has broken out and retested the previous support levels. It is expected to reject from the current level and test lower levels.
TF : 1h
Entry : $1.209
Target : $1.127
SL : $1.268