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Grounding AI on Enterprise Data

Name: Grounding AI on Enterprise Data
Availability: InStock

The final discipline every serious AI developer in 2026 needs: how to put an LLM on top of real business data without it hallucinating columns, joining wrong tables or leaking data across access boundaries.

Learn the semantic layer as a discipline, build one with dbt Semantic Layer, wire it to an LLM through MCP and take a working system through governance, versioning and observability.

Ends with a capstone building a trusted AI-on-data system for Alpine Financial, a fictitious SA fintech.

6 hours

30 lessons

Certificate included

Instructor

JP Instructor

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This course includes:

6 hours of comprehensive lessons
Knowledge-check quizzes in every module
Certificate of completion
Lifetime access to all content
Mobile-friendly responsive design

Course Curriculum

Module 1: Why LLMs Fail at Enterprise Data

The failure mode. Why pointing an LLM at your data warehouse without scaffolding leads to hallucinated columns, wrong joins and confidently incorrect numbers. Set up the problem the rest of the course solves.

6 lessons

The Text-to-SQL Problem

Preview

8 minutes

Preview Lesson

Real Failures From the Wild

7 minutes

Why "Just Give it the Schema" Does Not Work

7 minutes

Semantic vs Syntactic Understanding

6 minutes

Introducing the Semantic Layer

8 minutes

Module 1 Quiz

5 minutes

Module 2: The Semantic Layer as a Discipline

Anatomy of a semantic model in detail. Entities, dimensions, measures, joins, business-friendly names, metric definitions and access control. Plus the 2026 tooling landscape and a decision guide.

6 lessons

Anatomy of a Semantic Layer

8 minutes

Business Names vs Source Names

6 minutes

Metric Definitions and Governance

7 minutes

Access Control and Row-Level Security

7 minutes

The 2026 Tooling Landscape

8 minutes

Module 2 Quiz

5 minutes

Module 3: Building Your First Semantic Layer

Hands-on with dbt Semantic Layer (primary) and Cube (alternative shown alongside). Set it up, define your first entity, add measures and dimensions, model joins, test and deploy.

6 lessons

Setting Up dbt Semantic Layer

7 minutes

Defining Your First Entity

7 minutes

Defining Measures and Dimensions

8 minutes

Modelling Joins and Relationships

7 minutes

Testing and Deploying

6 minutes

Module 3 Quiz

5 minutes

Module 4: Wiring an LLM to Your Semantic Layer

The text-to-metric pattern, how LLMs call a semantic layer, prompt patterns for grounded querying, clarification loops and the cost + latency reality.

5 lessons

Text-to-Metric: A Better Pattern Than Text-to-SQL

7 minutes

How LLMs Call a Semantic Layer

7 minutes

Prompt Patterns for Grounded Querying

7 minutes

Clarification Loops, Latency and Cost

6 minutes

Module 4 Quiz

5 minutes

Module 5: Production Concerns

What changes when this goes to production. Governance as the business evolves, versioning metric definitions, access control end-to-end, auditability and lineage, observability for semantic-layer queries.

6 lessons

Governance as the Business Evolves

6 minutes

Versioning Metric Definitions

6 minutes

Access Control End-to-End

7 minutes

Auditability and Lineage

6 minutes

Observability for Semantic-Layer Queries

7 minutes

Module 5 Quiz

5 minutes

Module 6: Capstone: Build a Trusted AI-on-Data System for Alpine Financial

End-to-end capstone. Model a semantic layer for Alpine Financial (fictitious SA fintech), deploy with dbt Semantic Layer, wire it to Claude via MCP, answer five real business questions, add observability, reflect on trust. Portfolio-eligible.

1 lessons

Build a Trusted AI-on-Data System for Alpine Financial

240 minutes

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