#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
#DYDX/USDT analysis :
#DYDX has broken through the trendline after bouncing back from the support zone. The price is expected to maintain its bullish momentum and test higher levels moving forward.
TF : 4H
Entry : $1.0456
Target : $1.2576
SL : $0.8977