#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
#KNC/USDT analysis :
#KNC is retracing towards the 200 EMA, indicating potential upside. The price is respecting the trendline and bouncing back, which suggests a good opportunity for a long entry. Previous highs will serve as target levels.
TF : 4H
Entry : $0.4198
Target : $0.4582
SL : $0.3939
#KNC/USDT analysis :
#KNC is currently in a downtrend, trading below the 200 EMA. The price is forming a pattern of lower lows and lower highs. At present, the price is facing resistance near the 200 EMA, suggesting a potential reversal from this point to maintain its bearish movement and establish a new lower low.
TF : 4H
Entry : $0.4419
Target : $0.4083
SL : $0.4589