#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
#LRC/USDT analysis :
#LRC has retraced to the previous resistance zone, which is now acting as support for the price. From this level, bullish momentum is expected, with the previous swing high serving as the target level for upward movement.
TF : 1D
Entry : $0.1700
Target : $0.3338
SL : $0.1300
#LRC/USDT analysis -
#LRC is currently experiencing a downtrend and is trading below the 200-day Exponential Moving Average (EMA). The price is undergoing a pullback and is anticipated to test the resistance zone close to the 200 EMA shortly before potentially declining further to retest the previous lows.
TF : 4h
Entry : $0.1890
Target : $0.1601
SL : $0.1994
#LRC has a symmetrical triangle pattern on Daily time frame,in the case of breakup we will see another huge pump again 🚀
❄️@signals_bitcoin_crypto❄️
❄️@Shadow_support0o❄️
#LRC bounced back from the Demand zone also touched the curve on Weekly time frame,we need a bullish momentum to breakup this pattern 🚀
❄️@signals_bitcoin_crypto❄️
❄️@Shadow_support0o❄️