#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
#CKB/USDT analysis :
#CKB has retested the previously respected zone. The price is anticipated to rebound from the current level and test higher levels.
TF : 1W
Entry : $0.006300
Target : $0.020440
SL : $0.004500
#CKB/USDT analysis :
#CKB has experienced a breakout and has successfully retested the previous breakout zone. It is expected to rise from the current level and test previous highs. A bullish momentum is anticipated in the near future.
TF : 1D
Entry : $0.01408
Target : $0.01916
SL : $0.01239