Why Your AI Model Shouldn't Be Doing the Database's Job

This post details how executing data integration and joins within the database engine, rather than within the AI model's context window, significantly reduces token costs and improves auditability. It introduces the concept of the SingleStore Context Engine, which caches execution plans for repeated queries, bypassing the language model entirely after the initial request. The post also identifies symptoms of model-based integration, such as high ingestion volume into the model's context window and token expenditure proportional to total data growth, and discusses trade-offs of a unified AI data architecture.