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Data Science
Towards Data Science·Jun 18·5 min read
Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each
TL;DR
When developing AI applications, obtaining structured outputs from language models (LLMs) is crucial for further processing. JSON Mode and Function Calling are two techniques that enable LLMs to return data in specific formats.
✦ Why It Matters
Engineers can leverage JSON Mode and Function Calling to enhance data processing in AI applications.
Key Takeaways
- Use JSON Mode for structured data outputs from LLMs.
- Implement Function Calling for interactive AI applications.
- Explore OpenAI's Structured Outputs for schema enforcement.
- Enhance data handling efficiency in AI systems.
Full Summary
In AI applications, relying solely on free-text responses from language models can be limiting, especially when structured data is required for further processing. JSON Mode allows LLMs to return outputs in a JSON format, making it easier to programmatically handle the data.
Function Calling, also known as tool use, enables LLMs to execute specific functions based on user input, providing a more interactive experience. OpenAI has introduced a stricter variant called Structured Outputs, which enforces schema compliance for even more reliable data handling.
These approaches help developers extract specific fields from text or images, populate databases, and trigger actions based on model responses. By implementing these techniques, engineers can significantly improve the efficiency and reliability of their AI applications.
Overall, structured outputs facilitate better integration of AI into existing systems.