#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
#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