Vice President Global Marketing, AI Content and Localization, Board Trustee, Brand strategist.
Can a technology called RAG keep AI models from making stuff up? Here's a quick dive into how RAG works and its potential benefits... RAG introduces an information-retrieval component to AI, enhancing its accuracy by pulling in external data. When a user queries an AI, RAG searches relevant sources, converts the query into a vector representation, and cross-checks it with external databases. This process augments the AI's response with up-to-date and accurate information, often citing sources for better reliability. Key Benefits: - Improves the accuracy of AI responses by leveraging external data. - Can update external data continuously without altering the underlying AI model. - Enhances domain-specific knowledge, making it valuable for specialized fields like medicine and history. Limitations: - While RAG mitigates the issue of AI "hallucinations," it’s not a perfect solution. - It helps direct AI to better sources but doesn't eliminate the fundamental issue of AI generating inaccurate information. #AI #Technology #Innovation #RAG #ArtificialIntelligence #DataScience #BusinessInnovation #ResearchAndDevelopment Melanie Peterson from RWS Group https://lnkd.in/epPdHB9D
