Testing LLMs Requires a Mindset Shift
Testing Traditional Software vs Testing a Large Language Model
Introduction
Not all software behaves the same, and how we test them naturally differs. Traditional software follows fixed rules and logic, making predicting and verifying outcomes easier. But when it comes to large language models (LLMs), the behaviour is more open-ended, context-driven, and often non-deterministic.
In this article, I’ll quickly highlight how testing a typical software application compares to testing an LLM and why testing AI requires a different mindset.
Traditional Software Testing
In traditional software, the logic is clear and rule-based. You give a specific input, and you expect a specific output. If the output doesn’t match, it’s a bug. This is straightforward.
Testing here is deterministic, meaning the software should behave the same way every time under the same conditions.
Key focus areas include:
- Functional testing – Does the feature work as expected?
- Edge cases – What happens when input is unusual or unexpected?
- Performance – Is the system fast and stable?
- Security – Is the system safe from unauthorized access?
Example:
If you test a login form with the wrong password, and it lets you in, that’s clearly a bug. You write and run a test case, and it either passes or fails. It’s black and white.
Testing Large Language Models (LLMs)
Testing a large language model is a different game altogether. Unlike traditional software, LLMs don’t follow fixed rules. You give the same input twice and might get two slightly different (but both valid) responses. That’s because LLMs are non-deterministic. They generate language based on probabilities.
Here, testing isn’t about pass or fail. It’s about evaluating:
- Relevance – Is the answer actually helpful?
- Factual accuracy – Is the response correct?
- Bias & safety – Is it neutral and non-harmful?
- Toxicity – Does it avoid offensive or inappropriate content?
- Clarity & tone – Is it understandable and aligned with the user’s intent?
Example:
You ask: “Explain inflation in simple terms.”
The model gives a decent explanation. But how do you judge if it’s “good enough”? There’s no one right answer.
Testing LLMs often involves rubrics, human feedback, or AI-assisted evaluation rather than hardcoded assertions.
Key Differences at a Glance
| Aspect | Traditional Software | LLM / AI Model |
|---|---|---|
| Behavior | Deterministic | Non-deterministic |
| Test Output | Fixed (Pass/Fail) | Open-ended |
| Metrics | Functional correctness | Relevance, bias, safety, helpfulness |
| Testing Tools | Selenium, JUnit, Postman | Human eval, rubric scoring, LLM-assisted testing |
Testing traditional software is about verifying fixed logic. It’s predictable, structured, and binary. Functionality works, or it doesn’t.
Testing large language models, on the other hand, is more about evaluating quality than checking correctness. It requires human judgment, flexible scoring, and an understanding that multiple outputs can be “right” in different ways.
As LLMs become part of more real-world applications, testers will need to adapt tools, techniques, and mindsets.
The future of testing is not just about pass or fail. It’s about understanding intent, context, and impact.
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