#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
#CHZ/USDT analysis :
#CHZ is currently in a downtrend and making new lows. Look for a price retracement to test the resistance zone for a short entry opportunity. Lower levels will be the target level.
TF : 2H
Entry : $0.04862
Target : $0.04200
SL : $0.05193
#CHZ/USDT analysis :
#CHZ is in an uptrend, trading above the 200 EMA. After the breakout of the previous swing high, the price is sustaining above it. The price is anticipated to continue its bullish momentum and rise higher. Wait for a pullback to the support zone for a long entry.
TF : 2H
Entry : $0.0535
Target : $0.0560
SL : $0.0519