#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
#ETHFI
Here's a closer look at the technical analysis:
• Volume has been steadily increasing over the past three days.
• The 5-day Exponential Moving Average (EMA5) has crossed over the 20-day EMA, indicating a positive trend.
• While the Relative Strength Index (RSI) is high in lower time frames, it remains relatively low on the daily chart.
The price has broken out of resistance with significant volume. If it can sustain this momentum without retracing back into the previous range, there's potential for further upward movement