#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
#RUNE/USDT analysis :
#RUNE has retested the resistance zone following a bullish impulsive move. The price is anticipated to bounce from the current level and test higher levels.
TF : 1W
Entry : $2.190
Target : $7.566
SL : $1.314
#RUNE/USDT analysis :
#RUNE has recently broken out of the 200 EMA with strong bullish momentum. The price is currently showing bullish price action. It is expected to rise higher. Before that, the price is expected to pull back and then rise higher. Wait for the price to test the support zone for a long entry.
TF : 2H
Entry : $3.813
Target : $4.233
SL : $3.603
#rune scalping opportunity 🧐
After the big dump, the previously formed liqudation void was closed. From here, it would either turn or dump would continue.
When we look at the short time frame, we see that it can start the rise by forming a failure swing.