#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
#DYDX/USDT analysis :
#DYDX has broken through the trendline after bouncing back from the support zone. The price is expected to maintain its bullish momentum and test higher levels moving forward.
TF : 4H
Entry : $1.0456
Target : $1.2576
SL : $0.8977