#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
#POND/USDT analysis :
#POND has shown a strong recovery from the support zone following a significant decline of -77% from its all-time high (ATH). The price has also successfully broken above the trendline that was previously acting as resistance, indicating a likely continuation of its bullish momentum toward reaching new all-time highs. A potential gain of +120% is anticipated from the current levels.
TF : 1W
Entry : $0.01966
Target : $0.04240
SL : $0.01358
#POND👀
CoinLegs algorithm detected Symmetrical Triangle at 1h chart 📊
Seems like we got a breakout. Stop Loss below the trendline 🛑
Target levels are on the charts.
#POND result
3rd target achieved in just 12 days ✅✅✅
One more huge quick profit 21.9%🤑💰🤑
👉 Still thinking? The more you wait more you lose profit
☎️ Contact @MichaelStrategiesVip for membership and grab next breakout signal
#POND result
1st target achieved in just 1 day✅
One more quick profit 7%💰🤑
👉 More quick profit signals available in premium channel. Hurry up 🏃♂👇
☎️ Contact @MichaelStrategiesVip