Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each

📰 Towards Data Science

Learn how to use JSON mode and function calling with LLMs to generate structured outputs and when to use each approach

intermediate Published 18 Jun 2026

Action Steps

Use JSON mode with LLMs to generate structured outputs by providing a JSON object as input and specifying the desired output format
Implement function calling with LLMs to generate structured outputs by defining a function that takes input and returns output in a specific format
Compare the advantages and disadvantages of JSON mode and function calling to determine which approach to use for a given task
Apply JSON mode and function calling to real-world problems, such as data processing and generation, to see the benefits in practice
Test and evaluate the performance of LLMs using JSON mode and function calling to identify areas for improvement

Who Needs to Know This

Data scientists and machine learning engineers can benefit from this knowledge to improve the performance and usability of their LLM models

Key Insight

💡 JSON mode and function calling are two approaches to generate structured outputs with LLMs, each with its own strengths and weaknesses

Key Takeaways

Learn how to use JSON mode and function calling with LLMs to generate structured outputs and when to use each approach

Full Article

Title: Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each

URL Source: https://towardsdatascience.com/structured-outputs-with-llms-json-mode-function-calling-and-when-to-use-each/

Published Time: 2026-06-18T18:00:00+00:00

Markdown Content:
# Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each | Towards Data Science

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Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each

Key Takeaways

Full Article

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