#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
#RUNE/USDT analysis :
#RUNE has retested the resistance zone following a bullish impulsive move. The price is anticipated to bounce from the current level and test higher levels.
TF : 1W
Entry : $2.190
Target : $7.566
SL : $1.314
#RUNE/USDT analysis :
#RUNE has recently broken out of the 200 EMA with strong bullish momentum. The price is currently showing bullish price action. It is expected to rise higher. Before that, the price is expected to pull back and then rise higher. Wait for the price to test the support zone for a long entry.
TF : 2H
Entry : $3.813
Target : $4.233
SL : $3.603
#rune scalping opportunity 🧐
After the big dump, the previously formed liqudation void was closed. From here, it would either turn or dump would continue.
When we look at the short time frame, we see that it can start the rise by forming a failure swing.