Ollama Structured Output: A Guide to Predictable JSON

8/12/2025

Unlocking Predictable AI: A Deep Dive into Structured Output with Ollama

Hey there! If you've been playing around with local large language models using Ollama, you've probably had moments of pure magic, followed by... well, moments where the output is a bit all over the place. It's the wild west of AI, right? You ask for a simple list, & you get a paragraph. You want a JSON object, & you get something that almost looks like JSON, but with a friendly "Here is the JSON you requested:" preamble that breaks your parser.

Fun times.

But here's the thing, for developers & anyone trying to build REAL applications on top of these models, that unpredictability is a killer. We need reliable, machine-readable data. We need structure.

Turns out, the folks behind Ollama heard our collective sighs of frustration. They've rolled out a game-changing feature: structured output. This is, honestly, one of the most significant updates to the platform. It allows us to force the model's output into a specific, predefined JSON schema.

This means we can finally get the reliability & consistency we need to build robust applications, from parsing documents to powering intelligent agents. So, grab a coffee, & let's dive into how you can get started with structured output in Ollama. It's pretty cool.

First Off, What Exactly is Structured Output & Why Should You Care?

Alright, let's break it down. At its core, structured output is about making an LLM's response predictable. Instead of getting a free-form text string, you get a response that follows a specific format you define. Most of the time, this format is JSON (JavaScript Object Notation), which is a lightweight, easy-to-read format for data interchange that's become the lingua franca of web development.

Think about it this way:

Without Structured Output: You ask a model, "Tell me about the main characters in Harry Potter." You might get a nice paragraph, a bulleted list, or even a short story. It's a gamble.
With Structured Output: You can tell the model, "Give me the main characters, & for each one, I want their name (a string), their age (an integer), & their magical abilities (an array of strings)." The model is then constrained to provide a JSON object that looks exactly like that.

This shift from unpredictable text to predictable data is HUGE. Here are a few use cases where this becomes an absolute necessity:

Data Extraction: Imagine you have a folder full of PDF invoices or scanned receipts. You could use a vision-capable model (like Llama 3.2 Vision) to "look" at these documents & extract key information like vendor name, total amount, & due date, putting it all into a clean, structured JSON format.
API Calls & Function Calling: If you're building an AI agent that needs to interact with other software, it needs to generate precise API calls. Structured output ensures the parameters are formatted correctly every single time.
Powering User Interfaces: If you're building a web app that displays information from an LLM, getting structured data makes it infinitely easier to populate UI components without complex & brittle string parsing.
Building Reliable Chatbots: When a customer interacts with a chatbot, the bot needs to understand the user's intent & extract key information. For instance, if a user says, "I want to book a flight from NYC to LA for next Tuesday," the bot needs to reliably identify the departure city, destination city, & date.

This last point is particularly important. For businesses looking to automate customer interactions, reliability is non-negotiable. That's where this technology really shines. For instance, a platform like Arsturn helps businesses create custom AI chatbots trained on their own data. By leveraging structured output, these bots can have much more reliable conversations. When a visitor asks a complex question, the bot can understand the query, fetch the right information, & present it back in a structured way, ensuring a smooth & helpful experience 24/7. It transforms the chatbot from a simple Q&A machine into a sophisticated tool for engagement & support.

Getting Your Hands Dirty: The Basics of Ollama's JSON Mode

Ollama has made it surprisingly easy to get started. The core of this new feature is the

format

parameter in the API. You can now specify

json

to tell the model you want a JSON output.

First things first, make sure your Ollama is up to date! You might need to restart it to pull the latest version. Once you're set, you can start experimenting.

The cURL Method: A Quick Test

The simplest way to see this in action is with a good old-fashioned cURL command in your terminal. You don't need to write any code for this.

Here’s how you can ask a model like Llama 3.1 to give you information about a country in a specific JSON format: