What defines a great AI system? Not the model. Not the tools. Not even the prompts. It’s the tests. AI is software, and software is only as good as its tests.
Table of Contents
- Assertions → Metrics
- Integration Tests → Pipeline & Model Interaction Checks
- Unit Tests → Single Prompt-Level Checks
- Regression Tests → Model Consistency Tests
- User Acceptance Tests → Human Preference & Alignment Testing
- Fuzz Testing → Adversarial Robustness Testing
- Coverage Tests → Data Distribution Checks
- Conclusion
- Related Articles
Your evaluation dataset is your test suite. Without it, you’re flying blind, stuck in vibe-driven development. You won’t know if your changes improve the AI or break it. You’ll guess. Everyone on the team will guess. You’ll fix one thing, break another, and spin in circles—at best. More likely, you’ll spiral downward.
If you hate wasted time and effort as much as I do, you make all your software testable. What if the requirements change and all the testing efforts are useless? That’s unfortunate. Tests won’t save you from bad requirements. They won’t stop you from building the wrong thing. But they will stop you from building the right thing wrong.
What do tests look like in AI? Let’s stretch the testing vocabulary and define what you need using familiar terminology.
Assertions → Metrics
Every test needs an assertion, and testing AI is no different. However, AI tests can pass partially—you might get 70% of 12% of responses matching expectations. Does that mean the test passes? While you can set a threshold for failure, we’re not focused on binary results. In machine learning, metrics don’t determine whether something works; they only determine whether one version is better than another. All machine models are, to some extent, broken. Yet some are still useful.
We don’t stop at measuring one metric. One metric won’t tell us what problem we have. For example, if you measure faithfulness to verify the AI’s answer is based on retrieved data, that metric alone isn’t sufficient to guarantee a correct answer. You can cite the proper sources and still derive a wrong conclusion.
With multiple metrics, it’s tempting to highlight only the one that improved. Last week, faithfulness got better. This week, consistency improved. But you didn’t break correctness or faithfulness, right? Right? You should pick one metric as a long-term target and use others only as hints on where to look and what to do when something isn’t right.
Integration Tests → Pipeline & Model Interaction Checks
The users don’t care that your model retrieved the right data if the conclusion AI derived from the data is wrong. The users need a correct final answer. Or the proper effect in the case of AI agents interacting with other software. In software engineering, we would call this an integration test. In AI, the integration test checks whether running the entire AI system on a given input produces the expected result.
Lucky for us, this is the easiest dataset to get. Even if you have to create the dataset manually, describing what you want to see for a given query is way easier than explaining why you want to see it.
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Unit Tests → Single Prompt-Level Checks
Testing a single LLM response reveals if each gear in your AI machine turns correctly. Instead of wondering why the whole system failed, you can spot the exact misstep. Now, you no longer worry whether the entire AI pipeline produces the right answer. Here, you check if you get the right answer for the right reason.
When you describe why you want to see something, you need to know what is inside the underlying data sources or what the tools used by the AI agent may produce for a given query. This is much harder to prepare but more valuable when debugging the AI pipeline. Having such detailed tests helps you pinpoint the exact cause of the problem. Think of debugging an 8-step process. Would you rather know only that the process failed or learn that at step 3, AI generated a faulty SQL query? Detailed testing shows you where to fix the problem.
Regression Tests → Model Consistency Tests
Good AI should be consistent – feed it the same input and get similar outputs. Simple? Yes. Overlooked? Often. Machine learning basics still matter: your model must balance bias and variance, just as always.
Depending on the business purpose of the AI system you build, some variance may be acceptable or even encouraged. But you should always control it and make a conscious decision whether the inconsistency you see isn’t an error.
Now, an important warning. A proper consistency test compares the results from multiple runs and needs a metric telling you whether the answers are similar. Testing AI consistency requires more than matching accuracy scores. Two runs can hit 90% accuracy while giving completely different answers. Real consistency testing needs precise metrics that compare the actual outputs. Don’t trust the shortcuts.
User Acceptance Tests → Human Preference & Alignment Testing
Many of the tests and metrics we use in AI are AI-based. We use AI to test AI. We’ve created a strange loop: AI testing AI. This doubles our risk – both systems can hallucinate. Worse yet, these AI tests might prize traits that humans find worthless. Are we measuring what matters, or just what machines can count?
The alignment tests should tell us if the LLM-as-a-judge we use returns the same verdicts as a human data reviewer. Do you see the implied consequence? You need data reviewed by humans. As much as it’s tempting to use AI for everything, you can’t align AI if there is nothing to align AI with.
You should also check if the answer produced by your AI system adheres to the content policy you defined. It’s another level of alignment. You have already aligned the AI judges, now you align the AI pipeline itself. And if you use AI to check if the AI’s output follows the content policy, guess what? Alignment!
Fuzz Testing → Adversarial Robustness Testing
People will try to break your AI system. Some break software for a living, and some do it for fun. When you release an AI-based system to the public, you will be swarmed by hordes of people who hate AI and take perverse pleasure in watching it fail. It’s inevitable.
Someone will eventually succeed. That’s inevitable, too. Even if they resort to the silly, beaten-to-death “AI can’t count R-s in strawberry” example.
You need to prepare your AI system for abuse like this, and to prove you are prepared, you need a dataset of adversarial inputs. You may decide your AI should keep going when it faces such input and still try to produce some valuable answer, or you can filter out such requests and show the user a generic “Content policy violation” message. Whatever you decide, you have to test it. The good news is that gathering adversarial data is easy. Just release the AI and wait a little while.
Coverage Tests → Data Distribution Checks
Did you test the right thing? Are your evaluation datasets similar to the actual production data?
If you start with synthetic datasets, the answer is no, but we do it on purpose to get started. Once you get the ball rolling, we replace the AI-generated dataset with real-world data. However, even if your entire dataset is a snapshot of real user queries and examples for intermediate steps derived from the real data, you may still have a problem with data drift.
GenAI systems live at the mercy of user behavior. And users change. Sometimes slowly, sometimes because a TikTok prompt hack goes viral. Suddenly, half your users adopt new habits. Your AI must adapt - but first, you need to spot these changes. Are you comparing test prompts with real user data? If not, you’re flying blind.
Conclusion
You can’t run away from testing.
The conclusion applies to all software. No matter if the code makes LLM calls or not. GenAI can write test code. But it can’t understand why a particular test matters. Can’t grasp business goals. Can’t reason about test strategy. Let AI write the boring parts, but keep the thinking human. If you can’t tell me why each test exists, stay away from my code.
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