Introduction to AI & LLM Testing

Introduction to AI & LLM Testing

Artificial intelligence and large language models (LLMs) are now part of the products QA engineers are asked to test — chatbots, AI-assisted search, content generators, and automated decision tools. Testing these systems draws on the same testing mindset as traditional software QA, but it adds a set of problems that classic manual and automated testing were never designed to handle.

This article covers:

1. What makes AI systems different to test
2. Non-determinism and why it changes how you write test cases
3. The main categories of AI testing
4. Where AI testing fits in the development lifecycle
5. How a QA engineer can start learning AI testing

What Makes AI Systems Different to Test

Traditional software is deterministic: given the same input and the same code, you get the same output every time. A login form either accepts a password or rejects it, and that behavior does not change between test runs. AI systems, especially those built on machine learning models, do not work this way. Their behavior is learned from data rather than written as explicit rules, which means the system can be technically 'working correctly' and still produce an answer nobody predicted.

This has three practical consequences for testing. First, there is rarely a single correct output to assert against — testers work with a range of acceptable answers instead of one expected value. Second, the system's behavior can drift over time as the underlying model is retrained or fine-tuned, so a test that passed last month is not guaranteed to pass today. Third, failures are often subtle: the system responds fluently and confidently even when it is wrong, so a tester has to actively look for errors rather than wait for the system to crash or throw a visible error.

Non-Determinism: Same Input, Different Output

Ask an LLM the same question twice and you may get two differently worded — sometimes contradictory — answers. This happens because most language models generate text by sampling from a probability distribution over possible next words, rather than always picking the single most likely one. Some systems allow this randomness to be reduced (often called 'temperature'), but it is rarely eliminated completely, and many production systems keep some randomness on purpose because it makes responses feel more natural.

For testing, this means a single test run proves very little. A test case that passes once could fail on the next run with identical input, and a test that fails once might be an anomaly rather than a genuine bug. Effective AI testing usually runs the same input multiple times and looks at the pattern of outputs — how often the system gives a correct, acceptable, or wrong answer — rather than treating one run as a definitive pass or fail.

The Main Categories of AI Testing

AI testing is usually organized around a few overlapping concerns, and a mature test strategy touches all of them:

• Functional accuracy — does the system produce correct, relevant answers for the tasks it was built for?
• Robustness — does it hold up against unusual, malformed, or adversarial input instead of breaking or producing nonsense?
• Safety and bias — does it avoid harmful, offensive, or discriminatory output, and does it treat different groups of users fairly?
• Consistency — does it give stable, non-contradictory answers to the same or similar questions?
• Performance — does it respond within an acceptable time and cost, especially under load?

Each of these needs a different kind of test design, and most real projects prioritize them differently depending on what the AI system is used for — a customer-facing chatbot puts more weight on safety and bias than an internal data-summarization tool does, for example.

Where AI Testing Fits in the Development Lifecycle

AI testing is not a single phase at the end of development — it happens at several points:

• Data testing, before a model is trained, to check that training and evaluation data is representative, balanced, and free of obvious quality problems.
• Model evaluation, where data scientists and QA jointly define benchmarks the model must pass before it is considered ready.
• Integration testing, once the model is wired into the actual product, to test the AI feature the way a user will actually experience it — through the UI, the API, and the surrounding application logic.
• Production monitoring, after release, because a model's real-world behavior can differ from what was seen in testing, and can change over time as user input patterns shift.

QA engineers are typically most involved in integration testing and production monitoring, working alongside data scientists and ML engineers who own the earlier stages.

Getting Started as a QA Engineer

You do not need a machine learning background to start testing AI features. The core testing skills — designing test cases, thinking in edge cases, documenting defects clearly, and being systematic about coverage — transfer directly. What changes is the mindset: instead of asserting on one expected value, you learn to define a range of acceptable outputs, run tests repeatedly rather than once, and pay close attention to how confidently a system states something that turns out to be wrong.

A practical way to start is to pick a feature you already use — a chatbot, a search assistant, an AI writing tool — and deliberately try to break it: ask it something outside its scope, give it contradictory instructions, or ask the same question five times and compare the answers. The patterns you notice from that kind of exploration are the foundation the rest of the articles in this section build on.

Further Reading

Tesvan has published several real case studies on AI testing challenges:

Defining “Correct” in AI Systems
Solving LLM Instability in Internet Marketplace Platforms
Retrieval-Augmented Factuality
Hallucination Testing In Chatbots