#jupyter_notebook
Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability.
https://github.com/langchain-ai/rag-from-scratch
#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