Beyond Static Evals: Catching Rare LLM Logic Bugs with Property-Based Testing and Python

In the rush to deploy Generative AI, many development teams fall into the "Happy Path" trap. You write a prompt, test it with three or four inputs, and if the Large Language Model (LLM) returns a coherent answer, you ship it. But LLMs are non-deterministic by nature. A prompt that works perfectly on Monday might hallucinate on Tuesday, or fail spectacularly when the user input varies slightly from your test cases.

For CTOs and engineering leads, this unpredictability is the primary blocker to moving from Proof of Concept (PoC) to production. Traditional unit testing—where input A always equals output B—doesn't apply here. Static evaluations (like MMLU benchmarks) only tell you how smart the base model is, not how reliable your application logic is.

To build enterprise-grade AI solutions, we need to borrow a technique from the world of functional programming: Property-Based Testing (PBT). In this post, we will explore why static evals are insufficient and how you can use Python to implement rigorous PBT pipelines that catch rare logic bugs before your users do.

The Trap of Static Evaluations in AI Development

Most AI development starts with static evaluations. You curate a dataset of 50 "golden" questions and answers, run your prompt against them, and calculate a similarity score. While this is a necessary first step, it is woefully inadequate for production software. Why?

• The Memorization Problem: LLMs are prone to overfitting on your test set. If you optimize your prompt to pass your 50 static examples, you aren't necessarily improving the general logic; you are just teaching the model to pass the test.
• The "Long Tail" of User Input: Real-world user data is messy. It contains typos, slang, conflicting instructions, and edge cases you didn't anticipate. Static evals rarely cover the infinite variance of production data.
• Stochastic Behavior: Even with the temperature set to zero, floating-point non-determinism in GPUs can lead to slight variations in output. A static string comparison test is brittle; if the model changes "The result is 5" to "The answer is 5," a static test fails, even though the logic is correct.

"Testing AI isn't about checking if the answer is exactly right every time; it's about checking if the answer is never catastrophically wrong."

This is where we see the highest failure rate in enterprise AI adoption. Companies at the Nohatek level know that reliability isn't about the 90% of the time the model works; it's about handling the 10% where it drifts.

What is Property-Based Testing for LLMs?

Property-Based Testing (PBT) flips the testing paradigm. Instead of verifying specific examples (Example-Based Testing), you verify invariants—properties that should always hold true, regardless of the input.

In the context of standard software, a property for a sorting function might be: "The output list must have the same length as the input list, and every element must be less than or equal to the next."

In the context of LLMs, we define properties based on the logic and structure of the response, rather than the exact text. Here are common properties we test for at Nohatek:

• Structural Integrity: Does the output parse as valid JSON? Does it adhere to the specific Pydantic schema required by the frontend?
• Sanity Bounds: If the user asks for a summary, is the output length strictly less than the input length?
• Safety Invariants: Does the output contain any PII (Personally Identifiable Information) that was not present in the input?
• Hallucination Checks: If the model extracts entities (e.g., names from a resume), does every extracted name actually appear in the source text?

By testing for these properties, we can generate thousands of synthetic inputs (fuzzing) and check if the model violates these rules. This reveals the "rare" bugs that static evals miss.

Implementing PBT with Python: A Practical Approach

To implement this in Python, we can combine the power of libraries like hypothesis (for generating diverse inputs) with instructor or pydantic (for structural validation). Here is a conceptual workflow for testing an LLM designed to extract order details.

First, we define the structure we expect using Pydantic:

from pydantic import BaseModel, Field
from typing import List

class OrderItem(BaseModel):
    product: str
    quantity: int

class OrderExtraction(BaseModel):
    items: List[OrderItem]
    total_items_count: int

Next, we write a property test. We want to verify that the total_items_count calculated by the LLM matches the sum of the quantities it extracted—a common logic error in LLMs.

import pytest
from hypothesis import given, strategies as st

# A mock function simulating an LLM call
def call_llm_extractor(text_input):
    # ... logic to call OpenAI/Anthropic ...
    pass

@given(st.text(min_size=20, max_size=500))
def test_order_logic_property(generated_text):
    # 1. Get the result from the LLM
    result = call_llm_extractor(generated_text)
    
    # 2. PROPERTY: Structural Validity
    assert isinstance(result, OrderExtraction)
    
    # 3. PROPERTY: Internal Consistency
    # The model's summary count must match the sum of items
    calculated_sum = sum(item.quantity for item in result.items)
    assert result.total_items_count == calculated_sum

In this scenario, hypothesis generates random text strings. While many will be nonsense, some will trigger edge cases in the LLM's parsing logic. If the LLM tries to hallucinate a total count that doesn't match the items listed, the test fails.

This approach allows us to run hundreds of test cases overnight. When a property fails, you have found a logic bug. You can then add that specific failing input to your static evaluation set to prevent regression. This cycle—Fuzz, Fail, Fix, Regression Test—is the gold standard for AI engineering.

Moving from Prototype to Production

Implementing Property-Based Testing is not just a technical exercise; it is a business imperative. When you deploy an AI agent that interacts with customers or manages internal data, the cost of a hallucination is no longer just a weird screenshot on Twitter—it is a potential security breach, a lost customer, or a corrupted database.

At Nohatek, we integrate these testing methodologies into our CI/CD pipelines. We don't just ask, "Does it look good?" We ask, "Does it respect the invariants of the business logic?"

For CTOs and decision-makers, demanding this level of rigor from your development teams (or your external partners) is crucial. It changes the conversation from "AI is magic and unpredictable" to "AI is software, and it can be tested."

As we move beyond the initial hype of Generative AI, the industry is settling into the hard work of engineering robust systems. Static evaluations are a good starting point, but they are not the finish line. By adopting Property-Based Testing with Python, you can uncover the subtle, rare logic bugs that jeopardize reliability.

Ready to build AI solutions that you can actually trust? At Nohatek, we specialize in cloud-native development and enterprise AI integration. We don't just build demos; we engineer resilient systems ready for the real world. Contact us today to discuss your next project.