How to Test Generative AI: Why the Answer Is In Your High School History Class

Testing generative AI systems requires a shift in mindset, tooling, and test strategy.

If you’re building with generative AI and wondering how to test it properly, the answer might surprise you.

You already know it.

Think back to high school history class. Not the multiple choice section, but the open-ended questions. The ones where two students could give completely different answers and still both be right.

That is exactly the challenge if you want to test generative AI.

The outputs are not fixed. The answers are not identical. But they can still be valid.

And that breaks almost everything we’ve built around automated testing.

Why Testing Generative AI Is Not Straightforward

Generative AI is trained to be flexible. Ask the same question twice, and you’ll get two different responses. They might say the same thing in spirit, but the words, tone, and structure will shift.

This is a feature, not a bug. It makes outputs more human, more usable, and more adaptive.

But from a testing perspective, it introduces a real problem. Most test automation works on exact string matches, which makes it ill-suited for evaluating the flexible, nuanced outputs of generative AI. You define the expected output, run the script, and compare. If it matches, great. If not, fail.

Now try that with a generative answer that changes every time. Automation sees a failure. You, reading the actual result, might see something perfectly fine.

That is the core disconnect.

The History Test Analogy

To test generative AI is more like grading essays in a history exam. You give students a prompt. They respond in their own words. The teacher evaluates if the response is accurate, coherent, and relevant.

There is no single right answer
Expression matters
Judgment is required

This is how to test generative AI should feel like. You are not validating static outputs. You are evaluating if the model understood the request and responded appropriately.

Why Traditional Test Automation Breaks

Let’s make it concrete.

Imagine your AI is asked: What were the main causes of the Industrial Revolution?

First run:
“The Industrial Revolution was driven by technological innovation, urbanisation, and access to coal and trade routes.”

Second run:
“It began as new machinery, population growth, and abundant resources transformed manufacturing and transport.”

Same idea. Different words. But a traditional test script sees two different strings and throws an error.

That is not scalable. It leads to false failures and missed context.

And it shows why knowing how to test generative AI needs a different mindset.

What Needs to Change

We need to move away from exact matches and build evaluation systems that understand semantic similarity and meaning.

That means combining automation with human review. Building semantic comparison into your test flows. Designing prompt coverage checks instead of static output checks. And involving actual subject matter experts when necessary.

It is slower than traditional testing. But it is the only way to get real confidence.

How to Test Your Generative AI Implementation

Here is a practical list to get started:

Define what good looks like
Use semantic similarity instead of exact match
Automate what you can, but review the rest
Use real-world prompts, not just test cases
Track variability, not just accuracy
Test for bias, tone, and harmful responses: these are critical according to OpenAI’s alignment and safety guidelines
Build continuous feedback into your loop

Final Thoughts

You cannot test generative AI like traditional software. It is not about right or wrong. It is about context, nuance, and whether the response makes sense.

You need to test like a teacher, not a compiler.

If your current automation strategy depends on perfect matches, it is not going to hold up. You need smarter validation, deeper review, and a mindset shift.

Sometimes, the best way to move forward with AI is to look back at how we evaluated knowledge before all this started.

Back when we still had to explain ourselves in full sentences.

Want to see how we apply this mindset in real-world Salesforce QA and test automation?
Get in touch with us — or explore our Salesforce QA services.