#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
Transforming the Used Goods Market
Investors were initially skeptical of Shopify, yet it grew to 4.5 million stores. A similar transformation awaits the $350 billion used goods market, where 93% of stores are still offline. Innovations using AI can reduce digitalization challenges by 95%. This presents an opportunity for a new platform to revolutionize online reselling. For more insights, check this article: Cointelegraph
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