#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
#AMP/USDT analysis :
#AMP is currently exhibiting an uptrend, consistently reaching new highs while trading above the 200 EMA. The price is now retracing toward the 200 EMA and a significant support level.
It is expected that the price will test this zone and subsequently rebound, which will allow the bullish momentum to persist and potentially lead to a retest of previous highs.
TF : 1D
Entry : $0.006750
Target : $0.011915
SL : $0.004950
#AMP/USDT analysis :
#AMP has broken above a previously tested resistance level and has bounced back after a retest. This suggests a continuation of its bullish momentum, with the price likely to test previous highs from the current level.
TF : 1D
Entry : $0.005420
Target : $0.013400
SL : $0.004180
#AMP has a trading range on 12H Time frame, we expect a good pump from bottom of this range,also after breakup will have another bullish movement again
📈
❄️@signals_bitcoin_crypto❄️
❄️@Shadow_support0o❄️