#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
#DODO/USDT analysis :
#DODO is currently in a downtrend, trading below the 200 EMA. Following an impulsive move, a retracement is underway. Price is expected to revert from the current level and move upward to test previous highs and the 200 EMA. Wait for a pullback in price for a long entry.
TF : 30min
Entry : $0.1200
Target : $0.1304
SL : $0.1150
#DODO result
1st target achieved in just 9 hours✅
One more quick profit 8%💰🤑
👉 More quick profit signals available in premium channel. Hurry up 🏃♂👇
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🌎 The dodo bird, once native to Mauritius, vanished by the late 17th century. Little was documented about its appearance or habits, leaving scientists puzzled. Recent studies of preserved bones and rare historical notes suggest the dodo was likely grayish with small wings and weighed up to 18 kilograms. ✨
#dodo⚡#extinction⚡#mysteries
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