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Building a sophisticated AI product can quickly become an expensive endeavor. You might spend hundreds of thousands of dollars developing your AI system, only to double that on testing and debugging. AI tools can be both a significant investment and a source of cost savings during these development and testing phases. While development is often the focal point of many AI projects, the testing phase is where friction, delays, and skyrocketing costs could occur. But with proper preparation and alignment, much of this time and money can be saved.
The primary reason AI testing can be so complex is simple: AI isn’t predictable in the same way traditional software is. Most people, especially those unfamiliar with the intricacies of machine learning, skip over this crucial factor during the planning phase, leading to misaligned expectations.
AI systems learn by processing large volumes of data to identify patterns, which contributes to their unpredictability.
When you’re building traditional software, you’re often working with predictable outputs. If you develop an e-commerce website, for example, you can accurately predict what will happen when a customer clicks “buy” or enters their shipping details. The system is designed to follow specific, rule-based logic that leads to a fixed outcome every time. Testing these systems revolves around ensuring those fixed outcomes are consistent.
With AI, particularly machine learning models, outputs aren’t always so precise. AI systems, particularly large language models (LLMs), work on probabilities. This means that while they are extremely good at providing high-quality approximations, they can’t guarantee the same answer every time. This often leads to a rude awakening when the AI you’ve spent so much time developing gives slightly different answers to the same question on different occasions.
And here’s the kicker: Fixing one response from an AI model could make ten other responses less reliable. This is why testing AI is a lot less about ensuring the same response every time and more about aligning with expectations on what “correct” looks like. Here’s how you can avoid falling into common testing pitfalls and optimize the process.
When planning your AI product, it’s essential to integrate testing into your strategy from day one. Misaligned expectations about what AI can and should do lead to confusion, wasted time, and ultimately, wasted budget. A significant number of projects fail simply because the people developing and testing the AI have different ideas about what success looks like.
Before diving into specific testing strategies, it’s helpful to clarify why AI systems can be so challenging to test. AI outputs are based on probabilities, which means they can fluctuate based on minor variations in the input data. While a traditional system can be easily debugged and corrected when it doesn’t work, AI systems aren’t quite so simple. You can’t always pinpoint an exact rule that caused the problem—AI systems are about patterns, not hard-coded logic. Applications like computer vision require rigorous testing due to their complexity and the critical nature of their outputs.
Testing AI systems without clearly defining what success looks like is akin to shooting in the dark. You must establish a well-defined evaluation suite to help guide you through the testing process. Having this blueprint can prevent unnecessary back-and-forth communication and clarify expectations across all teams involved. For applications involving natural language processing, success criteria must account for the AI's ability to understand and generate human language accurately.
Some questions to consider when setting up your testing strategy include:
To effectively test AI, you need repeatable, reliable test cases that allow you to measure the impact of changes consistently. While AI’s outputs might be probabilistic, you can still set up scenarios that mimic real-world use cases and validate the model’s performance based on these cases. For generative AI applications, test cases should cover a wide range of prompts to ensure the AI can generate accurate and relevant content.
For instance, if you’re building an AI chatbot, develop a diverse set of test queries that cover a wide range of potential inputs. The goal here isn’t for the chatbot to always give the exact same answer but for it to consistently offer an accurate and helpful response. Your test cases should reflect the diversity of real-world inputs and provide clear criteria for evaluating whether the AI is meeting expectations.
One of the best ways to ensure that your AI model is performing as expected is to involve subject matter experts in the testing phase. If you’re developing a system that’s designed to replace or improve a manual task, for example, the employees who are familiar with that task will have invaluable insights into what “correct” looks like.
Just as artificial neural networks are designed to mimic the human brain, involving human experts ensures that the AI's performance aligns with human expectations and standards.
It’s easy to overlook the importance of these domain experts during the testing phase, but their input is crucial. They can provide nuanced feedback about the AI’s performance that purely technical testers might miss. This ensures that your AI solution not only works but delivers results that align with user expectations.
Beyond the technical aspects of testing, it’s important to remember that data quality remains one of the most critical components of AI success. Before any testing begins, you should have a clear understanding of the data your system is using and ensure that it’s accurate, unbiased, and representative of the real-world scenarios your AI will encounter.
Poor data quality will result in poor performance, no matter how well-structured your tests are. Invest time and resources upfront into cleaning and refining your data, and your AI system will be far easier to test and refine.
Testing AI systems isn’t a one-time event. It’s a continuous process. AI models change over time as they are retrained with new data, so you need to maintain an ongoing testing process that ensures the system remains reliable.
Setting up automated testing pipelines is one way to streamline this process. These pipelines can automatically run predefined test cases whenever changes are made to the model or the data it’s trained on. This saves time, reduces manual effort, and helps you catch problems before they become major issues.
Testing AI is complex, but it doesn’t have to be overwhelming. By asking the right questions, setting up clear evaluation criteria, and involving domain experts in the process, you can significantly reduce friction and avoid costly delays. Testing isn’t just a technical requirement—it’s a way to ensure that your AI system meets expectations, performs reliably, and ultimately delivers the value you intended.
Answer these questions and establish a robust testing process before you even begin building, and you’ll be well on your way to a successful AI project.
