Data granularity plays a crucial role in how we understand, evaluate, and improve AI systems. In AI testing, granularity isn’t just a data feature but directly impacts how accurately we measure model performance.
In this article, we’ll explore why data granularity matters in AI testing, the different levels of granularity you can use, and how to choose the right level for each stage of development. We’ll also highlight the common mistakes and how to avoid them, helping you create more accurate AI test strategies.
1. Data Granularity In AI Testing
Data granularity refers to the level of detail contained within a dataset. It can range from detailed data (individual user actions, historical logs, etc) to general data (overall trends, summarized metrics, etc).
In traditional analytics, granularity determines how deeply you can analyze trends. But in AI testing, granularity plays an even more critical role. It defines how precisely you can evaluate AI behaviors, uncover edge cases, and trace failures back to their root cause.
2. How Important Data Granularity Is In AI Testing
When you're testing an AI system, the level of detail in your data doesn’t just influence the results. It also shapes how well you understand the AI’s behavior in real situations.
Model Comparisons
Two AI systems can only be compared fairly if they’re tested with data at the same level of detail. Otherwise, the comparison becomes biased. For example, you give two AI systems different sets of data and request them to generate new test cases. One is detailed with requirement descriptions, while another is only with a few bullet points and notes. As a result, two models generate different output quality. It is not because one model is smarter, but because it was given more information to work with. This is especially important in some AI testing techniques, such as Pairwise, to ensure the comparisons are objective and meaningful.
Catching Edge Cases
Many AI failures happen in the tiny details. When you only provide the AI with general, simplified information, many edge-case issues never appear during testing. They only surface later, when real users interact with the system in unpredictable ways. The absence of these issues in early tests doesn’t mean they don’t exist. It simply means your data was too oversimplified to reveal them. For example, imagine testing an AI that validates shipping addresses. If you only provide clean, well-formatted examples, the model may look perfectly accurate. But once you include real-world variations (missing apartment numbers, slightly reordered fields, etc), you will see where the model struggles. Detailed data helps expose these weaknesses early, before they become customer-facing issues.
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Efficient Testing
More granularity isn’t always better. Sometimes, highly detailed data creates unnecessary complexity and slows down your testing efforts. For instance, if you’re checking whether an AI can identify the defect trends in a testing report, you don’t need the entire article with every paragraph included. A short summary may be enough to verify the model’s understanding. This can help you save more time and resources in the AI testing process.
3. The Three Levels of Granularity
Now that we understand why granularity matters, the next step is choosing the right level for your testing goals. Not all granularity is the same; too much or too little detail can distort your results or slow down your process.
To make this easier, data granularity can be grouped into three levels: high, intermediate, and low. Each serves a different purpose in AI testing. In the next section, we’ll look at what each level means and when to use it.
High (Fine) Granularity
High (Fine) granularity means your data is extremely detailed. Every action, field, or element is captured and treated individually. For example, they can be the detailed requirement documents, user flows, acceptance criteria, etc. This level is useful when you need to understand exactly how an AI system behaves or where it fails. You can use these data for debugging model errors, testing edge cases, etc.
Intermediate Granularity
Intermediate granularity provides a balanced level of detail. The data isn’t overly complex, but it includes enough information for meaningful analysis. They can be requirement summaries, brief explanations, key notes, etc. This is the most commonly used level in AI testing because it offers clarity without overwhelming the system. Those data can be applied in comparing models, evaluating model performance, etc.
Low (Course) Granularity
Low (Course) granularity uses broad or summarized data. It removes fine details and focuses on the big picture. It comes in handy for quick checks or high-level evaluations where detail isn’t necessary. Some examples of these data are pass/fail results, testing outcomes documents, etc.
4. How To Choose The Proper Granularity
Choosing the right granularity is not about picking “more detail” or “less detail”. The key is selecting the level that aligns with your testing purpose. The ideal granularity depends on what you want to learn and how deeply you need to evaluate AI output.
Choose high granularity when you're in the early development stage, where understanding the AI’s behavior in detail is essential. At this stage, small mistakes have a big impact, and every step of the AI’s reasoning needs to be visible.
Intermediate granularity becomes most valuable during the pre-production stage, where you need reliable, consistent inputs to test how well the AI performs under typical real-world conditions. It gives the AI enough context to perform meaningful tasks without overwhelming it or slowing down the testing process.
You should select low granularity once the AI reaches the production stage, where you primarily want quick insights rather than deep analysis. At this point, you’re looking for broad patterns and indicators that tell you whether the system is generally healthy. This level is best for broad assessments rather than deep evaluation.
5. Common Mistakes In Practice
Even with a solid understanding of granularity, many teams struggle to apply it effectively in real AI testing workflows. There are some common mistakes teams encounter when testing AI.
Using One Granularity Level for Everything
A frequent mistake is using only a single level of granularity throughout the entire testing process. Teams often rely on whatever data they used at the start and apply it to every stage, from development to post-production.
Different stages require different levels of detail. Early development needs detailed data to uncover reasoning errors, while production monitoring benefits more from general summaries that highlight trends. Using one granularity everywhere results in either over-testing simple tasks or under-testing critical scenarios.
Ignoring Granularity When Investigating Failures
When an AI system produces an unexpected or incorrect output, teams often focus solely on the result. They overlook a key question: Was the input at the right level of detail?
Sometimes, it is not about the models you are using. Instead, there could be problems with the data you input for AI. In other words, investigating output without reviewing input is like troubleshooting a device without checking whether it was plugged in.
Assuming More Data Automatically Means Better Testing
Another common misconception is believing that providing more details will always improve testing outcomes. While detail is valuable in the right scenarios, it’s not a universal solution. Aforementioned, too much data could slow down the AI performance and testing process. In many cases, a concise and focused input produces a more reliable test result than a large, detailed one.
Final thoughts
Choosing the right level of data granularity is essential for meaningful and reliable AI testing. The level of detail you provide shapes how accurately you can evaluate model behavior and uncover potential issues. In the end, effective AI testing isn’t about using more data; it’s about using the right data at the right stage. By adjusting granularity thoughtfully, you can achieve clearer insights, faster testing cycles, and more dependable AI performance.
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