#jupyter_notebook#ai#llm#llms#multi_modal#openai#python#rag
Retrieval-Augmented Generation (RAG) is a technique that helps improve the accuracy of large language models by fetching relevant information from databases or documents. This approach ensures that the model's responses are based on up-to-date and accurate data, reducing errors and "hallucinations" where the model might provide false information. For users, RAG offers more reliable and trustworthy responses, allowing them to verify the sources used to generate those responses. This method also saves resources by avoiding the need to retrain models with new data.
https://github.com/FareedKhan-dev/all-rag-techniques
#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