#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
#ERN/USDT analysis :
#ERN is currently in an uptrend, trading above the 200 Exponential Moving Average (EMA). The price has recently bounced back and broken out above the trendline, suggesting a potential continuation of the uptrend. The price is anticipated to move upside and test the swing high level.
TF : 30min
Entry : $1.704
Target : $1.992
SL : $1.620
#ERN/USDT analysis :
#ERN is currently in a bearish trend, characterized by a series of lower lows (LLs) and lower highs (LHs) while adhering to the trendline. The price is anticipated to continue this direction, testing lower levels in the near future.
TF : 1D
Entry : $2.230
Target : $1.610
SL : $2.608
#ERN/USDT analysis :
#ERN has broken out above the 200 EMA and is currently consolidating above it. The price is expected to sustain its bullish momentum and is likely to continue its upward trajectory.
TF : 4h
Entry : $2.277
Target : $2.554
SL : $2.088