How to Spot and Prevent AI Hallucinations in Software Testing

Picture asking an AI assistant a direct question and getting back a confident, well-organized, completely wrong answer. No hedging, no "I'm not certain," just a fluent response that happens to be invented. Anyone who has spent real time with large language models has seen it. Software has always had bugs, so a wrong answer by itself is nothing new. What makes this different is that the model sounds exactly as sure of itself when it's wrong as when it's right.

For teams building or testing software with AI somewhere in the loop, that space between confidence and correctness is where the real risk lives. As we've covered in our look at using GPTs and LLMs for software test automation, these models can genuinely accelerate QA work, but only when someone is consistently checking what they produce. 

In this article, we'll walk through what AI hallucinations actually are, why they happen, how they show up in day-to-day testing, and what it takes to keep them away from your users.

What are AI hallucinations?

Before we get into the real-world examples and causes, it’s important to define hallucinations as a whole. According to IBM, AI hallucinations are cases where an AI system produces outputs that sound plausible but are factually wrong, irrelevant, or entirely fabricated. In practice, this can mean a large language model or other generative tool presenting fake facts, invented studies, nonexistent URLs, or incorrect details about real people and companies, all with the same authoritative tone it uses for accurate answers. The effect isn't limited to text, either. Image, audio, and video generators can produce the same kind of confident nonsense in their own formats.

Notably, it helps to separate a hallucination from an ordinary mistake. A mistake is usually a wrong detail or an outdated fact, and it tends to be traceable back to a bad assumption or a gap in context. A hallucination is fabricated content with no grounding in real data. 

The hallucination is harder to spot because it mimics real patterns. The invented case law has proper-looking docket numbers, the fake study has a plausible title and author, the made-up API returns exactly the kind of response you'd expect. Hallucinations can sound right even when they are completely unverifiable, which is what makes them dangerous in a testing context, where "sounds right" can be the cause of legal repercussions.

Why do AI hallucinations happen?

To spot hallucinations reliably, it helps to understand where they come from. There are two angles here: how the model works, and what we feed it.

What makes the model hallucinate?

A generative model doesn't know what's true. It predicts the next token (roughly, the next word or word-piece) based on patterns it observed across a massive body of training text, and it optimizes for what's statistically plausible rather than what's correct. When the information it needs is sparse, ambiguous, or missing, it fills the gap with whatever best fits the pattern. It's also why a model can produce a paragraph that reads as coherent and expert while corresponding to no real data or events. Some causes of hallucination can be:

A gap in the training data. However large a training set is, it can't cover everything, and the more specialized or obscure a topic, the thinner the model's exposure to it. Ask about one of those corners and the model will usually reach for a plausible-sounding answer rather than admit it doesn't have one. Any errors or biases already baked into the data make things worse, since the model treats them as ordinary patterns worth repeating.

Absence of comprehension. A model can assemble a grammatically flawless answer without any grasp of what it means, and the fallout isn't always academic. New Zealand supermarket Pak'nSave found this out when its meal-planning bot recommended a recipe that would produce toxic chlorine gas and presented it as a refreshing drink. Nothing about the wording looked off on the surface, which is precisely how an unsafe answer slips past a casual reader.

The way the text gets built. A model writes one token at a time, each choice locked in before the next, with no built-in moment to stop and reconsider. An early wrong turn tends to snowball into a confidently incorrect conclusion instead of being caught partway through, and longer inputs compound the problem: as earlier context scrolls out of the model's limited working window, the thread of what it's saying starts to drift. 

How does the user impact AI hallucinations?

The quality of the AI's outputs is tied directly to the quality of the user's inputs, a theme we return to often in our writing on how ChatGPT helps modern testers. Vague prompts invite the model to guess at what we expect as outputs.

There's also a subtler trap here. Research from OpenAI on why language models hallucinate makes the case that these models are trained and evaluated in ways that reward confident guessing over honest uncertainty. So when a prompt implies an answer should exist, like asking for "five reasons" when only two are real, the model is more likely to invent the rest than say it doesn't know.

What AI hallucinations look like in software testing

Hallucinations aren't only a chatbot problem. They show up in the exact workflows QA teams are starting to hand to AI, and they tend to take a few recognizable shapes. IBM groups them into three broad types that map cleanly onto testing work:

• Factual hallucinations state something objectively false about the world, like inventing a statistic, a browser behavior, or an API response that doesn't exist.
• Contextual hallucinations happen when the model is supposed to answer from specific material you provided (a spec, a requirements doc, a ticket) but instead answers from its general training. The output looks reasonable, yet the context you handed it doesn't actually support it.
• Consistency hallucinations are internal contradictions within a single session. Namely, the model says a feature behaves one way early on, then assumes the opposite later.

Translated into daily QA, these produce failures like:

• Test cases for functionality that isn't there. Ask a model to generate tests from a requirement and it may confidently cover a flow the product doesn't have, or write automation scripts that look valid and then fail on execution.
• Invented selectors, methods, and endpoints. When generating or maintaining automation code, models can reference locators, functions, or even software packages that don't exist. This one carries a security edge. Specifically, a hallucinated package name is a supply-chain risk if an attacker has registered that name, so installing the model's suggestion pulls in malicious code.
• Misstated standards. Ask about an accessibility requirement under WCAG or EN 301 549 and a model can assert a rule that isn't in the standard. Since compliance work has real legal weight, a fabricated requirement is not a harmless error, but a costly one.
• Fabricated verdicts. A model that confidently reports a test passed when it didn't, or missed a real defect, is producing what QA already knows as a false positive or false negative. We've written a full breakdown of why false positives and negatives are so costly, and an AI-generated verdict that nobody verifies is one more path to the same damage.

This is the practical reason we treat AI output as a first draft to be checked rather than a source of truth. The model is a fast, tireless assistant, and it is also perfectly capable of being fluently, confidently wrong.

How to spot AI hallucinations

The single most useful demonstration is also the simplest. Ask a language model how many times the letter "R" appears in the word "strawberry;" the answer is three, spelled out at positions three, eight, and nine. For a long time (and even in the time of writing this) models, surprisingly, answered two, as seen below:

The cause here isn't classic fabrication from missing data. It's a tokenization limitation. The model never sees individual letters, only tokens (chunks like "straw" and "berry"), so the letters were effectively gone before it could count them. Newer, reasoning-focused models often get "strawberry" right now, but the same failure resurfaces with less common words, spelling a word backwards, or similar character-level tasks.

The “strawberry” test is worth keeping in mind because it shows the core problem in miniature: the model's fluency tells you nothing about whether the answer is correct. A response can be articulate, well-formatted, and wrong. That's the mindset to carry into every AI-assisted task.

From there, spotting hallucinations comes down to a handful of habits:

• Verify every citation, statistic, and URL against a primary source. If a model names a study, a case, a figure, or a link, confirm it exists and says what the model claims. Fabricated references are one of the most common and most damaging hallucination types.
• Be extra skeptical of suspiciously specific details. Exact numbers, named authors, and precise docket numbers can be signs of confidence, not correctness. Specificity is easy for a model to generate and easy to trust.
• Watch for internal contradictions. If a model's later statements don't square with its earlier ones, treat the whole thread as unreliable until you've checked it.
• Confirm the model actually used your context. When you've supplied a spec or document, check that the output reflects it rather than the model's general training.

How to prevent AI hallucinations

You can't remove hallucinations entirely. They're inherent to how generative models work, and the broad consensus across the research is that they can be reduced but never fully driven to zero. The realistic goal is control, and there's a well-established set of levers for it, each with its own trade-off to weigh.

The business case for that last row is well documented; IBM's Institute for Business Value found that 83% of surveyed executives agree governance is essential for AI deployment, yet only 4% currently have robust frameworks in place to manage it, a gap echoed in a separate 2026 IBM study finding 77% of organizations still lack basic AI governance capabilities.

No single lever drives the hallucination rate to zero, which is why the strongest setups stack several of them and keep human review as the final gate before anything ships.

Risks of ignoring AI hallucinations

It's tempting to file hallucinations under "quirks" and move on. The record from the past few years argues otherwise, across three kinds of damage.

Misinformation 

OpenAI's Whisper speech-to-text model, increasingly used in hospitals, has been found by an Associated Press investigation to invent content that was never spoken, including nonexistent medical treatments, even as tens of thousands of medical workers rely on Whisper-powered transcription. 

At the lighter end, Google's AI Overview famously suggested adding nontoxic glue to pizza sauce to help cheese stick, an answer traced back to an old joke post. Those examples land very differently in terms of harm, yet both trace back to the same underlying mechanism, and each erodes trust in the system that produced it.

Legal exposure

In 2023, a US lawyer used ChatGPT to draft a court filing and ended up citing entirely fictitious cases, complete with invented names and quotations, which drew sanctions once the court checked them. 

In 2024, a British Columbia tribunal held Air Canada liable after its website chatbot fabricated a refund policy; the airline argued the chatbot was a separate entity responsible for its own answers, and the tribunal rejected that outright. 

And in 2025, Deloitte agreed to refund part of a roughly A$440,000 report prepared for the Australian government after researchers found fabricated citations and a made-up quote from a court judgment, with the firm later disclosing it had used a generative AI tool. Regulations and courts don't draw a line between "we didn't think about that case" and deliberate negligence. A fabricated fact that causes harm is still exactly that, no matter the source.

Reputation and trust

When Google first demonstrated its Bard chatbot, a single wrong answer about the James Webb Space Telescope in a promotional video coincided with roughly $100 billion wiped off parent company Alphabet's market value as investor confidence dropped. The Deloitte episode carried a quieter version of the same cost: a paid expert report built partly on unverified AI output damages the credibility of every report that follows it. 

Rebuilding that kind of credibility is slow and costly, and hallucinations do their damage at exactly the point where users have decided to rely on you.

Keeping AI honest

Hallucinations will not disappear with the next model release. They come from the way generative AI works at a basic level, so the responsible approach is to design around them instead of waiting for them to go away. The teams getting real value from AI in testing ground it in trusted data, match the model to the stakes involved, and support all of it with governance.

That's the approach we bring to every engagement. AI works well in the workflow as an accelerator, with human expertise on top of it as the check on what it produces. The dependability comes from that verification layer, not from the model getting things right on its own.