Taming the Poet: Enforcing Strict JSON Schemas for Enterprise Workflows
Large Language Models (LLMs) are incredible poets. They can write sonnets, summarize novels, and draft emails that sound convincingly human. But in an enterprise workflow, we don’t need a poet. We need a data entry clerk . When AI becomes part of a business process—processing invoices, updating a CRM, routing support tickets—the output can’t be creative. It has to be machine-readable . If your downstream API expects a JSON object and the model responds with: “Here is the JSON you asked for: { … }” your system crashes. Not because the model failed, but because the extra text breaks the parser. This is the reliability gap. Prompt engineering (“please only output JSON”) is statistically unreliable. And at scale, “statistically unreliable” becomes “guaranteed to fail.” So the goal isn’t to ask more politely. The goal is to force the format . Here’s how to implement strict JSON enforcement on Databricks Model Serving.