#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
#DEXE/USDT analysis :
#DEXE is currently in an uptrend on the higher time frame (HTF). The price is in an accumulation phase, consolidating sideways. A breakout from this consolidation is anticipated soon, which would likely continue the bullish rally and test the swing high. We should look for potential pullbacks to establish long entries.
TF : 1W
Entry : $8.260
Target : $18.100
SL : $6.340
#DEXE/USDT analysis :
#DEXE is currently facing resistance from the 200 EMA on the daily time frame. On the 4-hour time frame, it has broken and retested the support zone. The price is now likely to continue its bearish momentum and test new lows.
TF : 4H
Entry : $8.461
Target : $7.953
SL : $8.783