Part two: Engineering context for enterprise AI

Rapid advances in AI models have improved reasoning, context capacity, and task execution, but reliable AI agents still depend on receiving accurate, relevant context rather than model capability alone. This is particularly important for Text2SQL systems, which must correctly interpret users’ questions, database schemas, business definitions, and SQL syntax, leaving multiple opportunities for errors. Context engineering, which emerged as an evolution of prompt engineering, focuses on supplying only the most useful information rather than overwhelming models with excessive detail, as too much context can reduce performance. For Text2SQL, governed semantic models can organize metrics, synonyms, join keys, data-value conventions, and verified queries, enabling agents to retrieve relevant information as needed; curated context has reportedly driven major accuracy improvements in analytics agents. Creating and maintaining this context is primarily an organizational challenge because data ownership often belongs to IT while business meaning resides with domain experts, requiring shared governance. Dataiku positions its technology-agnostic semantic models above individual databases and within a collaborative environment, aiming to make evolving definitions sustainable and accessible across technical and business teams.

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