The Digital Press

Building an AI data analyst requires more than just text-to-SQL capabilities; it demands multi-step workflows, strong context encoding, and integration with external tools and data. Using a semantic layer like Malloy helps encode business logic, improve SQL reliability, and enable precise, function-based interactions with large language models (LLMs).

Why it matters: Unlocking existing data value is key for teams, requiring AI that understands business context and performs complex, multi-stage analysis.

The big picture: Semantic layers formalize business meaning, reducing errors and enabling AI to generate accurate SQL and Python code aligned with enterprise logic.

The stakes: Without precise retrieval and validation, AI analytics risk hallucinations, wrong queries, slow responses, and brittle outputs in production.

Commenters say: Readers appreciate the detailed insights on bridging demos to production and express interest in Malloy's implementation and automating semantic model creation.