#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
🪙 32,137 #BTC ($2.18 billion) went from the wallet of the #Mt․Gox exchange that collapsed in 2014 to an unknown address — the market reacted with a drop, suggesting that payments to the exchange's creditors could begin at any moment.
⚫️The Black Swan arrived unexpectedly... wait for new comments, despite the unpleasant surprise, the situation may become an opportunity to enter the market and make money on non-negative growth.
😙 The reasons for the fall of the # bitcoin exchange rate below $61,000
The unemployment rate was 4.3%, which is higher than expected, indicating a possible recession
The Bank of Japan raised the interest rate for the first time in 17 years, which led to an outflow of investments from risky assets
Increased geopolitical tensions (fear of a major world war)
😏Continued distribution of #BTC from #Mt.Gox and #Genesis