Taming Non-Determinism: Building Reliable AI Systems with Pydantic and Structured Outputs

There is a specific moment of panic known only to developers building their first generative AI application. You spend hours refining a prompt, you get the perfect response in the playground, and you deploy it. Then, two days later, the application crashes. Why? Because the Large Language Model (LLM) decided to return a JSON object with a trailing comma, or worse, it decided to apologize for being an AI model instead of returning the requested data.

This is the challenge of non-determinism. LLMs are probabilistic engines—they predict the next likely token, not the next logical truth. While this creativity is perfect for writing poetry, it is catastrophic for integrating with SQL databases, REST APIs, or strict frontend interfaces.

For CTOs and engineering leads, the transition from "cool demo" to "enterprise software" hinges on solving this reliability gap. In this guide, we explore how to tame this chaos by enforcing strict data contracts using Pydantic and the modern ecosystem of Structured Outputs.

The Stochastic Gap: Why Regex Won't Save You

In traditional software development, functions are deterministic. If you input 2 + 2, you expect 4. If you send a malformed request, you expect a specific error code. LLMs operate differently. They are stochastic by nature, meaning the same input can yield slightly different outputs based on the model's temperature and internal probabilities.

For a long time, developers tried to bridge this gap with prompt engineering—begging the model to "Please return only JSON" or "Do not include markdown formatting." Then, they would write complex Regular Expressions (Regex) to parse the output. This is fragile. A model update or a slightly unusual user input can break these parsers instantly.

The shift from prompt engineering to software engineering requires treating LLM outputs not as text to be read, but as data to be validated.

When building AI agents that need to trigger actions—like booking a flight, querying a database, or updating a CRM—text is useless. You need structured data types: integers, booleans, enums, and nested objects. This is where the concept of Data Contracts becomes essential. We must force the probabilistic engine to adhere to a deterministic schema.

Enter Pydantic: The Standard for Data Validation

If you are working in the Python AI ecosystem, Pydantic is likely already part of your stack. It is the most widely used data validation library for Python, and for good reason. Pydantic allows you to define data models using standard Python type hints.

Instead of writing validation logic manually, you define the "shape" of your data. Here is a practical example of a data contract for an AI application that extracts information from customer emails:

from pydantic import BaseModel, Field
from typing import List, Optional
from enum import Enum

class PriorityLevel(str, Enum):
    HIGH = "high"
    MEDIUM = "medium"
    LOW = "low"

class TicketExtraction(BaseModel):
    customer_name: str = Field(description="The name of the user filing the complaint")
    issue_summary: str = Field(description="A concise summary of the problem")
    priority: PriorityLevel
    mentioned_products: List[str] = Field(default_factory=list)
    requires_human_intervention: bool

This class does three powerful things:

• Type Enforcement: It ensures priority is one of the three allowed Enum values. If the LLM generates "Urgent", Pydantic will flag this as an error.
• Documentation: The Field descriptions aren't just for developers; modern AI frameworks pass these descriptions to the LLM so it understands what it is looking for.
• Parsing: It handles the conversion of JSON strings into Python objects automatically.

By defining the schema before we call the AI, we establish a contract. The AI isn't just generating text; it is attempting to satisfy a specific class definition.

Orchestrating the Handshake: Structured Outputs in Practice

The industry has recognized that JSON reliability is paramount. Consequently, major model providers like OpenAI and Anthropic, along with orchestration libraries, have introduced native support for Structured Outputs.

When using OpenAI's latest models, for example, you can pass your Pydantic model directly into the API call. The model is then constrained to output tokens that match your schema. This isn't just post-processing; the model's actual generation process is guided by the schema.

Here is how this looks in a modern implementation using the OpenAI SDK:

completion = client.beta.chat.completions.parse(
    model="gpt-4o-2024-08-06",
    messages=[
        {"role": "system", "content": "Extract the ticket details from the email."},
        {"role": "user", "content": email_content},
    ],
    response_format=TicketExtraction,
)

message = completion.choices[0].message
if message.parsed:
    print(message.parsed.priority)
else:
    print("Refusal or failure to parse")

In this workflow, the message.parsed object is an actual instance of your TicketExtraction class. You get IDE autocompletion, type safety, and the confidence that if the code executes, the data is valid.

Why is this a game-changer for Enterprise AI?

• Reduced Latency: You don't need to make a second API call to "fix" broken JSON.
• Security: By enforcing Enums and strict types, you reduce the risk of prompt injection attacks causing the model to output malicious payloads.
• Maintainability: Your data schema lives in your codebase, version-controlled alongside your application logic, not hidden inside a text prompt.

The transition from experimental AI to production-grade systems requires a shift in mindset. We must stop treating LLMs as magic black boxes and start treating them as components in a software architecture—components that require strict inputs and outputs.

By leveraging Pydantic and Structured Outputs, we can effectively tame the non-determinism of AI. This allows developers to build applications that are not only intelligent but also robust, predictable, and ready for business-critical operations.

At Nohatek, we specialize in building enterprise-grade cloud and AI solutions that prioritize reliability and scalability. If you are looking to modernize your infrastructure or build AI applications that actually work in production, let's talk.