#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
🤔#LDO Financial indicators for Lido in 2025 show that total revenue decreased by 23% on an annual basis to $40.5 million, with net staking commission revenue amounting to $37.4 million.
The DAO is evaluating a potential LDO buyback program in the second quarter of 2026. link
#LDO/USDT analysis :
#LDO is in a downtrend, currently rejecting from the 200 EMA resistance after going through a correction phase. It is expected to continue its bearish momentum and test the previous swing low. Wait for the break of the $1.091 level downside for a short entry.
TF : 15min
Entry : $1.091
Target : $0.993
SL : $1.153
#LDO/USDT analysis -
#LDO has recently broken out of the channel after facing rejection from the 200 EMA in a downtrend. It is now expected to continue its downward momentum and test new lows.
TF : 2H
Entry : $1.530
Target : $1.398
SL : $1.604