#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
🚀 Back to Back profits are made in the Premium Group
🎯🎯#GENIUS/USDT has covered all the target to give a Profit of 167% to all Premium Members
👁🗨Contact @futurechief to enter the Premium Futures & SPOT Group for daily gain
🇺🇸 Gemini joined 125+ companies urging Congress to keep the #GENIUS Act as it was written.
These parties believe that changes would undermine legal stablecoin rewards and innovation.
➖➖➖➖➖➖➖➖➖
📣@cryptonewstel
✨Vip join⭐️