#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
📊#XVG – POSITION UPDATE
Placed initial bids in the $0.0076 – $0.0078 zone.
This area is acting as a key decision level.
If the weekly candle closes above $0.0078, it would mark the first confirmed breakout since 2024.
In that case, continuation setups will be considered.
Until then, patience matters.
#XVG/USDT analysis :
#XVG is currently consolidating sideways. The price has tested and held support. A rebound from this support level is anticipated, with the target being the resistance level.
TF : 4h
Entry : $0.005600
Target : $0.007415
SL : $0.004360
✅✅ 27% Profit on #XVG/USDT for our Premium Members on Binance Futures, ByBit USDT, KuCoin Futures, OKX Futures
👁🗨Contact @primemod to enter the Premium Group & make daily profit on SPOT & FUTURES Signals