Grounded AI in Data Warehousing: How to Make Your LLM Stop Lying [Engelstalig]

Large Language Models are rapidly becoming embedded in BI platforms, where they now generate SQL and business answers directly for end users. This fundamentally changes the risk profile of analytics. Silent hallucinations at scale are now an operational risk.

Most current AI-in-BI implementations still rely on prompt tuning and soft guardrails. This may work in demos, but it does not survive audits, regulatory review or real production pressure. If AI systems are not structurally bound to governed data and formal business definitions, errors are inevitable.

This workshop shows how to build grounded AI analytics directly inside Snowflake using Cortex Analyst. Participants will deploy the official Snowflake Labs reference implementation in their own Snowflake trial environment and extend it with semantic grounding, safe text-to-SQL enforcement and full audit traceability.

Working hands-on with semantic models and controlled query generation, you will learn how to bind natural language questions to approved business definitions, prevent unauthorized data access and trace every AI-generated answer back to the underlying warehouse execution. The result is an AI analytics system that is explainable and fit for production use. This architecture pattern is demonstrated in Snowflake during the workshop, but the same control structure applies to any governed warehouse environment.

The workshop combines architecture, implementation and controlled failure testing. You leave with a working grounded AI assistant and the design patterns needed to harden it for real-world enterprise environments.

Learning Objectives

By the end of this workshop, participants will be able to:

• Understand why hallucinations occur specifically in BI and text-to-SQL systems
• Deploy and operate a working grounded AI analytics assistant in Snowflake using Cortex Analyst
• Bind natural language questions to formal semantic models and business definitions
• Implement safe text-to-SQL pipelines with schema, metric and access policy enforcement
• Prevent silent metric drift and grain violations in AI-generated queries
• Design and implement full audit traces from user prompt to warehouse execution
• Evaluate AI analytics systems for trust, compliance and regulatory defensibility.

Who is it for?

Roles that will benefit most from this workshop include:

• Senior analytics engineers and data platform engineers
• Data architects and information architects responsible for semantic layers
• BI platform owners and analytics leads
• AI engineers building LLM-powered analytics inside the warehouse
• Data governance, risk and compliance professionals working with AI systems
• Technical leads accountable for production AI behavior.

This workshop is technical by design. Participants should be comfortable with SQL, data warehouse concepts and semantic modeling.