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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
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
#Tech#AI#Ecommerce#UsedGoods#Innovation#AIIntegration#MarketGrowth#Digitalization#Reselling#Investment#Shopify#Startup#Entrepreneurship#Revolution#OnlineSelling#Future#Trends#Retail#Fintech#VC