#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
#ATA/USDT analysis :
#ATA is currently finding support above the 200-period exponential moving average (200 EMA) within the support zone. The price is anticipated to test this zone and maintain its bullish momentum to reach the previous swing high.
TF : 4h
Entry : $0.0900
Target : $0.0983
SL : $0.0850
Currently, #ATAUSDT is compressed within a falling wedge pattern, a classic bullish reversal signal.
Should #ATA fail to bounce back from the $0.0820-$0.0700 support, our eyes will be on the next critical level at $0.0580. Historically, this level has been a stronghold, and the probability of a rebound here is notably higher.
But if $ATA breaks below these key support levels, the bears might take control, potentially leading to a bearish continuation.