#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
#HIGH/USDT analysis :
#HIGH is currently trading above the support zone on the higher time frame. The price is demonstrating a bullish bounce from this area, indicating a strong potential for continued upward momentum and a test of higher levels ahead.
TF : 1W
Entry : $1.744
Target : $4.440
SL : $1.250