The AI’s Fact-Checking Sidekick
Posted: Sun Feb 09, 2025 8:34 am
The key lies in the ability to process and analyze data as it’s generated. Real-time analytics and AI are driving streaming data adoption. A recent survey found that real-time analytics south africa rcs data is the leading current use case (71%) for adopting streaming data systems. Nearly three in four respondents cited the development of AI/ML systems as the biggest driver of streaming data adoption. This shift toward real-time data generation and consumption is driving the need for AI systems that can keep pace.
RAG:
One of the most promising approaches to integrating streaming data with AI is through retrieval-augmented generation (RAG) databases. RAG systems allow AI models to query external knowledge bases for the most up-to-date information before generating a response.
These RAG databases help deal with the fact that the models were trained on old or generic data, and in some cases, old data will not give you the best recommendation based on the current data that’s coming in. You can think of RAG as an AI’s personal fact-checker, always ready to double-check information before giving a result.
By implementing RAG, organizations can significantly reduce hallucinations and improve the accuracy of their AI outputs. This is particularly crucial in fast-moving industries where decisions need to be made based on the most current information available.
RAG:
One of the most promising approaches to integrating streaming data with AI is through retrieval-augmented generation (RAG) databases. RAG systems allow AI models to query external knowledge bases for the most up-to-date information before generating a response.
These RAG databases help deal with the fact that the models were trained on old or generic data, and in some cases, old data will not give you the best recommendation based on the current data that’s coming in. You can think of RAG as an AI’s personal fact-checker, always ready to double-check information before giving a result.
By implementing RAG, organizations can significantly reduce hallucinations and improve the accuracy of their AI outputs. This is particularly crucial in fast-moving industries where decisions need to be made based on the most current information available.