#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
#GRT/USDT analysis :
#GRT has broken out of its trendline with strong momentum following a bounce back from the support zone. The price is expected to maintain its bullish momentum and test previous highs. The current level presents a favorable entry opportunity.
TF : 1W
Entry : $0.2023
Target : $0.3446 and $0.4902
SL : $0.1450
#GRT/USDT analysis :
#GRT is currently in a downtrend, making lower lows (LLs) and lower highs (LHs), and is trading below the 200 EMA. The price is facing rejection from both the 200 EMA and the resistance zone. It is expected to decline from this point and potentially test the previous swing low.
TF : 1D
Entry : $0.1682
Target : $0.1294
SL : $0.1940
💰#GRT has a falling wedge pattern on 8H Time frame, we expec it will pump alot in the case of breakout, waiting now..⌛️
❄️@signals_bitcoin_crypto❄️
❄️@Shadow_support0o❄️
💰#GRT bounced back from the Support zone on 12H Time frame, we are waiting for breakup and pullback to the broken trendline 💫
❄️@signals_bitcoin_crypto❄️
❄️@Shadow_support0o❄️