#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
✅✅ 86% Profit on #JUP/USDT for our Premium Members on Binance Futures, Bitget Futures, ByBit USDT, KuCoin Futures, OKX Futures
✔️✔️Trade has been exited in great profit
👁🗨Contact @primemod to enter the Premium Group for daily gain
🚀 70% Profit on #JUP/USDT for our Premium Members
👆🏻👆🏻These are terrific profit and we continue to be the best
👁🗨Contact @primemod to enter the Premium Group & make daily gains on Spot & Futures Market