#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
#QI/USDT analysis :
#QI has broken out and successfully retested the previous swing high resistance. The price is currently finding support at this zone. It is anticipated that the price will bounce back from here and test previous highs.
TF : 1W
Entry : $0.01650
Target : $0.03380
SL : $0.01080
#QI
https://www.binance.com/en/trade/QI_USDT
Buying Zone 195-200
Sell
🤑 220
🤑 250
🤑 250-280
🤑 280-310
🚀 310-350 & above
Currently facing R if broken x2
we can expect
Bullish above 178🔼
Death zone below 178🔽
#QI👈
Call given here
Hit 52 fourth selling range
Target 1 to 4 done ✅
57% safe profit
If you invested 1 btc it's now 1.57 btc now 🤑🤑
Signal before pump @ low level
Always trade with us for max profit 😊🤑💃
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👀#ATA#FIO#GTC#NTRN#PHB#QI#RDNT#BNB Binance Will Extend the Monitoring Tag to Include ATA, A2Z, FIO, GTC, NTRN, PHB, QI & RDNT on 2026-03-13 RDNTUSDT: 0.00571 #Binance#announcement