#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
🎯🎯#AIXBT/USDT has covered all the target to give a Profit of 201% to all Premium Members
👁🗨Contact @futurechief to enter the Premium Futures & SPOT Group for daily gain
✅✅ 125% Profit on #AIXBT/USDT for our Premium Members on On Binance Futures, Bitget Futures, ByBit USDT, KuCoin Futures, OKX Futures
👆🏻All Profit Targets Successfully Completed
👁🗨Contact @primemod to enter the most powerful premium group & make daily gains
AIXBT Tokenomics
Key aspects of #AIXBT tokenomics (at the time of writing) include:
Total Supply: 1 billion AIXBT tokens
Circulating Supply: 854 million tokens
Market Capitalisation: $85 million
All-Time High: $0.9426
Holders of AIXBT tokens gain access to the aixbt Terminal, a premium market intelligence platform designed for deep-dive crypto analysis.