Why AI needs a database

The text discusses the limitations and potential of AI-powered language models, such as large language models (LLMs), in handling structured data compared to databases. While LLMs are adept at processing and generating language from text data by tokenizing it into small units, they face challenges with structured data, like financial transactions, due to inefficient tokenization and lack of precise recall. Unlike databases, which provide exact and real-time data retrieval, LLMs generate probabilistic responses and struggle with large datasets due to fixed context windows and lack of persistent memory. The text argues that instead of replacing databases, AI models should integrate with them to leverage their strengths in accessing and interpreting structured data dynamically. It explores alternative methods like vector search and Retrieval-Augmented Generation (RAG) for unstructured data, but emphasizes that for accuracy and real-time analysis, particularly with structured data, direct database querying remains superior. The text concludes by suggesting that the synergy of AI and databases enables effective, accurate, and cost-efficient data interactions, highlighting that AI is not yet poised to replace databases but can significantly enhance their utility.