#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
#FIDA/USDT analysis :
A retracement to the 200 EMA is anticipated for #FIDA. Price has broken through a trendline and a minor resistance level, successfully retesting the zone. It is expected to rebound from the current level and move upward to test higher levels.
TF : 2H
Entry : $0.1187
Target : $0.1325
SL : $0.1090
#FIDA/USDT analysis :
#FIDA has rebound from the support zone after multiple tests. The price is anticipated to continue rising and challenge previous highs.
TF : 1D
Entry : $0.2267
Target : $0.2900
SL : $0.1840
#FIDA result
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