#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
#FUN/USDT analysis :
#FUN has established a breakout from its trendline after rebounding from the support zone. The price is anticipated to continue rising from its current level and is likely to test the previous swing high.
TF : 4H
Entry : $0.003263
Target : $0.003644
SL : $0.003008
#FUN/USDT analysis :
#FUN is currently consolidating within a resistance zone near the 200 EMA. A price rejection is expected from there, and a continuation of the downtrend is anticipated. Wait for the price to break below the trendline for a short entry.
TF : 2H
Entry : $0.00314
Target : $0.00287
SL : $0.00326