Learn how JSON mode can help you ensure your inference output conforms to a regular expression or JSON schema.
Contact our team to learn how you can leverage JSON mode in your AI applications with Telnyx.
In the dynamic landscape of artificial intelligence (AI) development, the ability to intelligently integrate AI into applications is a valuable asset. One of the key tools enabling this integration is JSON mode. In this blog, we’ll take a look at what JSON mode is, how it differs from function calling, and offer some tips on how to effectively leverage JSON mode for structured inference outputs.
JSON is a lightweight data-interchange format that offers developers an easier, more flexible way to structure complex data. It's particularly beneficial in AI applications where users often need a language model to translate between natural language and structured data.
With JSON mode, the LLM response is guaranteed to be valid JSON.
On top of this, developers can dictate the specific format and data types of the response with Telnyx’s guided JSON support. The result of guided JSON is a more structured and predictable output that can be tailored to specific applications.
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The main difference between function calling and JSON mode lies in the conformity of the schema output.
JSON mode ensures valid JSON syntax but does not ensure a specific schema. This is best when you want some structure, but also would like a language model to have a lot of freedom in deciding how to structure things.
Guided JSON ensures valid JSON as well as conformity to a JSON schema. This is best when you have very strict expectations for the fields and types of your data.
Function calling ensures conformity to a JSON schema while giving the language model the choice of which functions to call (or whether to call a function in the first place). This is best when you want the language model to have the freedom to choose between natural language and structured data with very strict expectations.
To make the most out of JSON mode it’s important to understand how to construct and interpret JSON-formatted requests and responses. Here are some top tips for understanding JSON mode:
JSON mode is versatile and can be used with lots of AI models—from natural language processing to image recognition to predictive analytics. JSON mode can handle the varied and complex data inputs these models require—it’s this adaptability makes it so powerful and suitable for nearly any AI application.
JSON mode can be used with every model in our open-source LLM Library, so you can experiment and test until you find the model that suits your needs best.
With guided JSON, you are guaranteed valid syntax, but a smarter model will provide higher-quality content within that syntax.
The Telnyx Inference API allows developers to fully harness the power of AI within their applications through a streamlined, JSON-based interaction. Once you have created your Telnyx account, it's easy to get started:
By embracing JSON mode, developers can leverage a powerful tool for AI integration, resulting in flexible applications.
Telnyx’s Inference API with JSON mode is ready to users elevate applications to the next level, offering the ease of integration and the breadth of functionality that modern AI-driven applications demand.
What is JSON mode in an LLM? JSON mode is an inference setting that forces a model to return strictly valid JSON for predictable parsing. By using an inference setting for guaranteed structured output, teams avoid brittle post-processing and reduce runtime errors.
How is JSON mode different from function calling? Function calling enables the model to choose a tool and emit typed arguments via a schema, as described in function calling, while JSON mode only constrains the output format. Use function calling when you need tool execution or API calls, and JSON mode when you only need structured data.
How do I enforce a schema with JSON mode? Provide a minimal, explicit JSON schema or exemplar in your prompt, and set temperature low to reduce variance. Validate with a strict parser, then retry with a repair prompt if parsing fails.
Which LLMs support JSON mode? Many modern models expose a response_format or JSON toggle, and open-source models often comply when guided by clear schemas. Models like Hermes 2 Pro Mistral 7B also support advanced function calling for structured outputs when JSON mode is unavailable.
Can I stream responses while using JSON mode? Strict JSON mode often disables token streaming because partial tokens can break validity, so most teams buffer until the JSON completes. If you require real-time speech with structured data, design for incremental metadata while audio streams as outlined in real-time media streaming for AI agents.
How do I handle invalid JSON or hallucinated fields? Run a JSON validator and schema check, then auto-repair by re-prompting with the parser error and a compact exemplar. Add token limits and whitelist keys to reduce drift, and fall back to safe defaults if retries fail.
When should I use JSON mode instead of free-form text? Choose JSON mode when the output feeds automation, databases, or API calls that demand deterministic structure. Use free-form text for conversational replies, creative copy, or exploratory reasoning where rigid structure is not required.
