Can We Automate the Testing of AI-generated content? — Part 1

During a recent discussion with Jeffrey Brown, a fractional CTO/Enterprise Architect, he inquired me about testing AI output.

I couldn’t think of an automated approach to testing AI-generated content then. So, after a few weeks of research, I decided to write this article and share my findings because someone else might have the same question.

Intro

Artificial intelligence (AI) and machine learning (ML) are transforming industries left and right, and software testing is no exception. As AI-powered systems create content, ensuring that the results are accurate and reliable becomes super important. But how do we automate testing something as dynamic as AI-generated content? The key is understanding the unique challenges of AI and ML systems and using smart strategies to tackle them.

Let’s Start with the Basics

AI is all about building systems that can handle tasks typically done by humans, like recognizing speech, making decisions, or even spotting patterns in data. ML, which is a branch of AI, helps these systems learn from experience instead of relying on a set of hard-coded instructions. This makes AI incredibly useful for tasks like automation and producing realistic results.

However, as AI systems get smarter and more complex, testing them thoroughly becomes more critical — and more complicated. That’s where automated testing comes into play, but it’s not always straightforward.

The Challenges of Testing AI Systems

Here are some reasons why testing AI systems can feel like solving a puzzle:

1. Unpredictable Results
AI systems can surprise you by giving different outputs for the same input. This non-deterministic behaviour makes it tricky to pin down what the “right” output should be, complicating automated testing.

2. Dependence on Data
AI models live and breathe data. Without high-quality, diverse data, it’s hard to train and test these systems properly. Plus, if the training data has biases, the results can also end up biased or just plain wrong.

3. The “Black Box” Problem
Many AI models, especially deep learning ones, work in mysterious ways. They might give you the right answer, but figuring out *why* they reached that answer can feel impossible. For example, why does your AI think a coupe is a sedan? It’s often hard to tell.

4. Constant Learning
AI models evolve over time as they learn from new data. This means you might have to re-test them regularly to ensure they’re still working correctly — a bit like chasing a moving target.

5. Dealing with Bias
Since AI often learns from human-labeled data, it can pick up our biases too. For instance, a voice recognition system might struggle with certain accents simply because its training data wasn’t diverse enough.

Traditional Approaches and Their Limitations

Currently, most AI testing relies on three primary approaches:

1. Human-in-the-Loop Validation

• Requires manual review of AI outputs
• Not scalable for large-scale applications
• Subject to human bias and inconsistency

2. Guardrails for Prompts

• Limits AI response scope
• May restrict useful model capabilities
• Still doesn’t guarantee deterministic behavior

3. Fail-Safe Functions

• Provides safety boundaries
• Often too rigid for complex use cases
• May miss subtle quality issues

These methods are certainly helpful, but they don’t quite solve the whole puzzle when it comes to automated testing for the unpredictable nature of AI outputs. It’s a tricky challenge and it encourages us to think outside the box for better solutions.

Moving Beyond Traditional Methods

We will need a testing solution that can -

• Handle non-deterministic outputs systematically
• Scale effectively with increasing AI deployment
• Provide meaningful quality metrics
• Operate without constant human oversight

Looking Ahead

The subsequent articles in this series will explore

1. Implementation of model-based testing using Spacy for content similarity validation.
2. Implementation of more advanced model-based testing using DeepEval
3. Practical approaches to automated metric evaluation
4. Integration strategies for continuous testing
5. Best practices for AI quality assurance

Wrapping It Up

By understanding the unique quirks of AI systems — like their unpredictability, data dependence, and potential biases — you can develop strategies to make testing smoother. Whether it’s creating robust test data sets, checking performance, or hunting for bias, these approaches can help ensure AI systems produce accurate, fair, and reliable results.

As AI continues to evolve, automating its testing will be key to building trustworthy applications across every industry. The future of testing is exciting, and with the right tools and strategies, we’re ready to meet the challenge.