Simplifying LLM Conditional Workflows Using Structured Output
Last Updated on December 29, 2025 by Editorial Team
Author(s): Nachiket Mehendale
Originally published on Towards AI.
Some LangGraph LLM workflows simply cannot work without a Structured Output class.
Imagine a pizza store collecting online customer feedback. Some reviews are happy, some unhappy. The store wants to automate responses: thank happy customers and investigate issues from unhappy ones.
In this article, we explored how structured output in LLMs can make workflows more reliable and actionable. Using a pizza store feedback scenario, we demonstrated a conditional workflow: positive feedback triggers a thank-you message, while negative feedback is analyzed to extract key facts such as Problem_Area, Customer_Mood, Problem_Seriousness, Priority_Level, and Expected_Action. By using structured schemas, we make the output predictable and easy to analyze. Always remember that structured output not only improves automation but also provides clean, actionable data that can be leveraged for insights, reporting, or further analysis. This approach is particularly valuable in workflows where clarity, consistency, and decision-making are crucial.
Read the full blog for free on Medium.
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