Generative AI Solutions with Databricks

Chapter 4. Other GenAI patterns on Databricks: Code generation, testing, and AI/BI data products

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Introduction: Expanding the scope of GenAI

Early enterprise adoption of generative artificial intelligence (AI) largely centered on “Human-to-Model” interactions—standalone chatbots where users manually bridged the gap between their data and the AI. While effective for isolated productivity gains, this approach inevitably creates “Context Silos,” where valuable insights remain trapped within individual chat sessions, disconnected from the governed pipelines and operational reality of the business.

To unlock true enterprise impact, the focus must shift to “System-to-System” GenAI. This paradigm moves models out of the chat window and embeds them directly into the core of data engineering, analytics, and business intelligence (BI) workflows.

On Databricks, this evolution elevates the platform from a traditional data lakehouse to a comprehensive Data Intelligence Platform. By integrating generative ...