#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
#ENS is rebounding from the support trendline of the descending channel, with the Ichimoku Cloud acting as a resistance barrier.
A decisive breakout above both the channel and the cloud would confirm bullish momentum.
#ENS/USDT analysis :
#ENS is currently in an uptrend, consistently reaching new highs while trading above the 200 EMA. The price is now retracing towards the 200 EMA and a significant support level. It is expected that the price will test this zone and rebound, which should support the continuation of bullish momentum and will lead to a retest of previous highs.
TF : 1D
Entry : $30.35
Target : $47
SL : $23.38
#ENS/USDT analysis :
#ENS is currently in an uptrend, trading above the 200 EMA. The price has recently bounced back from the 200 EMA, suggesting a continuation of its bullish momentum and potential testing of higher levels. For a long entry, it is advisable to wait for a retracement to optimize the entry point.
TF : 30min
Entry : $43
Target : $48
SL : $38