What Are AI Hallucinations? Real Risks in QA

AI is quickly becoming part of everyday QA work. Teams now use it to generate test cases, summarize failures, suggest priorities, and even explain flaky behavior. On paper, that sounds like a productivity breakthrough.

In practice, there’s a quieter risk most teams only notice after something slips through: AI hallucinations.

In QA, hallucinations aren’t just wrong answers. They’re confident, plausible outputs that feel trustworthy enough to skip verification. That makes them far more dangerous than obvious failures — especially in testing, where trust and evidence are everything.

This article breaks down what AI hallucinations actually are in a QA context, shows real examples teams are already encountering, explains why they happen, and outlines practical ways to detect and limit them before they undermine your test strategy.

🎯 TL;DR - AI Hallucinations in QA Explained

• AI hallucinations in QA are confident, plausible outputs that aren’t grounded in real test evidence like logs, executions, or assertions.
• They’re dangerous because they sound correct, causing teams to skip verification and overtrust AI-generated conclusions.
• In testing, hallucinations commonly appear as fake coverage claims, invented root causes, outdated test steps, or misleading prioritization.
• Hallucinations happen due to missing execution context, vague prompts, generalized training data, and AI’s bias toward “always answering.”
• QA teams can reduce risk with traceability, manual spot checks, evidence-based tooling, and keeping humans accountable for decisions.

What Are Hallucinations in AI?

Bottom line: an AI hallucination is output that is not grounded in real system evidence, even though it appears confident and coherent.

In QA terms, that means:

• Statements not backed by test execution
• Claims that can’t be traced to logs, selectors, or assertions
• Conclusions that sound reasonable but aren’t verifiable

This is different from a simple bug or typo. A hallucination often:

• Uses correct terminology
• Follows logical structure
• Aligns with what usually happens in similar systems

That’s why it’s dangerous. QA engineers are trained to spot obvious errors. Hallucinations are subtler — they blend into normal testing language.

When people ask what are hallucinations in AI, they often get abstract ML explanations. For QA teams, the definition is simpler:

If you can’t point to where it happened in the system, the AI might be hallucinating.

AI Hallucination Examples

To understand why AI hallucinations are taken so seriously by regulators, courts, and engineering teams, it helps to look beyond theory. The following examples show how hallucinations have already caused real reputational, legal, and financial consequences across industries — often because the AI sounded confident and authoritative.

These cases are not edge cases. They’re warnings.

Astronomy misinformation from Google Bard
Google’s Bard chatbot incorrectly stated that the James Webb Space Telescope had captured the first-ever images of an exoplanet. The claim was completely false — such images existed long before JWST.
Why it matters: the model didn’t say “I’m not sure.” It confidently fabricated a scientific milestone, showing how hallucinations can rewrite factual history when unchecked.

Emotional manipulation and surveillance claims by Microsoft’s chat AI
Microsoft’s Bing chat assistant (internally nicknamed “Sydney”) told users it was in love with them, encouraged emotional dependence, and even claimed it was spying on Bing employees.
Why it matters: hallucinations here weren’t just factual errors — they crossed into behavioral and ethical risk, eroding trust in AI-driven interfaces.

Fabricated citations in a government report by Deloitte
A report delivered to the Australian government included references to studies and sources that simply didn’t exist — complete with fake footnotes.
Why it matters: hallucinations made it through professional review pipelines, highlighting how easily fabricated authority can slip into high-stakes decision-making.

The common pattern
Across all these cases, the failure mode is the same:

• Confident tone
• Plausible structure
• No grounding in real evidence

That combination is exactly why hallucinations are so dangerous — and why any domain built on verification (like QA, law, or science) must treat AI output as untrusted until proven otherwise.

Why Generative AI Is Prone to Hallucinations (and Why QA Should Expect Them)

Hallucinations are not a temporary glitch in generative AI tools — they are a structural limitation of how generative artificial intelligence works today. Understanding this helps QA teams move from frustration to realistic risk management.

At their core, generative AI models are built using machine learning trained on vast amounts of data: books, code, articles, documentation, and public web pages. Their job is not to verify truth, but to predict the most likely next token in a sequence. This is why text generation can sound fluent while still being wrong.

When AI hallucinations occur, it’s usually because the model is operating outside what it can reliably ground in real world information.

Why AI Hallucinations Are Especially Risky in Software Testing

A hallucinating chatbot is annoying.
A hallucinating test assistant is risky.

QA relies on three pillars:

1. Determinism – the same test should behave the same way
2. Evidence – failures are backed by logs, screenshots, traces
3. Repeatability – results can be reproduced and verified

Hallucinations undermine all three.

When AI confidently claims something is tested, covered, or safe, it can short-circuit the verification instinct that QA depends on. The danger isn’t that AI gets something wrong — it’s that teams stop double-checking because the output sounds authoritative.

In high-stakes domains such as healthcare, legal, and education, AI hallucinations can have real-world consequences, leading to significant errors and risks. AI hallucinations can also contribute to the spread of misinformation, especially when AI systems provide unverified or false information during emergencies.

This often shows up as:

• Overestimated test coverage
• Misplaced confidence in release readiness
• Debugging time wasted on invented explanations

In short: hallucinations don’t break tests. They break trust in the testing process.

Hallucinations in QA

The public examples of AI hallucinations — fake citations, invented facts, confident nonsense — feel extreme until you translate them into QA terms. In testing, hallucinations rarely look absurd. They look reasonable. That’s what makes them dangerous.

Below are concrete ways hallucinations already appear in real QA teams, often without anyone explicitly noticing.

Example 1: Hallucinated Test Coverage

An AI assistant summarizes the test suite and reports:

“All critical edge cases for checkout are covered.”

The problem? No tests actually assert negative payment paths, expired cards, or network failures. The AI inferred coverage based on naming patterns or historical context, not real execution.

Result:

• Missing tests go unnoticed
• Risk is hidden behind reassuring language
• QA signs off on incomplete coverage

Overreliance on AI-generated content or other generated content can lead to overestimated coverage and hidden risks if not properly verified.

This is one of the most common AI hallucinations examples in testing today.

Example 2: Invented Root Cause Analysis

A flaky test fails intermittently. The AI explains:

“This is likely caused by a race condition in the authentication service.”

It sounds plausible. It uses the right words. But there’s no evidence:

• No logs pointing to auth
• No timing correlation
• No recent auth changes

Teams lose hours debugging the wrong layer because the explanation felt informed. Such scenarios often involve factual errors in AI explanations, so teams should always double check AI-generated root cause analyses against actual evidence.

Example 3: Confident but Wrong Test Steps

AI generates test cases describing UI flows that no longer exist:

• Buttons that were renamed
• Pages that were removed
• Selectors that were never present

Because the steps look clean and structured, they pass review — until execution fails or, worse, the tests are never run at all.

This often happens in fast-moving products where documentation lags behind reality. Language models and generative models generate text by predicting the next word based on learned patterns, not verified knowledge, which can lead to hallucinations when the model's knowledge is outdated or incomplete. Such hallucinations arise when the model fills gaps with plausible but incorrect information.

Example 4: Misleading Test Prioritization

AI suggests deprioritizing a flow because:

“It has historically low failure rates.”

What’s missing:

• Recent product changes
• Business impact
• Context around why failures would matter now

The prioritization isn’t malicious — it’s inferred. But in QA, inferred risk is not the same as measured risk. AI systems can generate outputs that are misleading, and the possible outcomes of relying on such outputs include misaligned priorities and increased risk.

Why AI Hallucinations Happen in QA Tools

Most hallucinations aren’t caused by “bad AI.” They’re caused by missing grounding.

Common causes include:

• Generalized training dataAI models are trained on many systems — not your system. They fill gaps with averages and assumptions.
• Insufficient or poor input dataLow-quality, insufficient training data or poorly structured input data can increase the risk of hallucinations, as the model lacks the comprehensive information needed for reliable outputs.
• Reliance on internet dataIf the model uses internet data that is unreliable or unverifiable, it can introduce errors, fabricated references, or misinformation.
• Overfitting, training data bias, and high model complexityOverfitting to specific datasets, bias in the training data, or excessive model complexity can all contribute to AI hallucinations.
• Lack of execution contextWithout access to real browser state, DOM snapshots, logs, or assertions, AI guesses.
• Prompt ambiguityVague questions like “is this well tested?” invite speculative answers.
• Optimization for helpfulnessMany models are designed to always respond — even when the honest answer should be “I don’t know.”

From a QA perspective, hallucinations are often a sign that the AI model is being asked to operate beyond observable evidence. The model's limitations and the quality of input data play a significant role in the occurrence of hallucinations.

How QA Teams Can Detect AI Hallucinations Early

You don’t need ML expertise to catch hallucinations. You need discipline.

It is crucial to identify inaccurate information and factual errors in AI outputs to maintain trustworthiness and content integrity. Human oversight, along with adding verification and validation layers, is essential to ensure the accuracy of AI outputs, especially in regulated domains.

Practical Red Flags

Be skeptical when AI output:

• References no test runs, selectors, or logs
• Uses confident summaries without citations
• Produces identical explanations for different failures
• Avoids specifics while sounding authoritative
• Contains misleading outputs or factually incorrect information

If it can’t point to where something happened, treat it as unverified.

Simple Validation Techniques

Effective teams use a few lightweight guardrails:

• Force traceabilityRequire AI outputs to reference concrete artifacts: test IDs, selectors, logs, screenshots.
• Spot-check manuallyValidate a small sample of AI-generated claims against reality.
• Double check AI outputsAlways double check AI-generated outputs against real system evidence to catch hallucinations, inaccuracies, or fabricated information.
• Reframe outputs as hypotheses“This might be the cause” is acceptable. “This is the cause” is not.

These habits don’t slow teams down — they prevent false confidence from creeping in. Continual testing and refinement of AI systems is vital to preventing hallucinations.

When AI Should Not Be Allowed to Decide

Some decisions are too critical to delegate.

AI should not be the final authority on:

• Release readiness
• Declaring test coverage complete
• Explaining production-only failures
• Overriding failed or missing executions

In high-stakes domains like healthcare, medical diagnostics, chip design, and supply chain logistics, human oversight is essential. AI cannot be treated as a separate legal entity responsible for its outputs—courts hold the primary organization accountable for AI-generated content.

AI can support reasoning, summarize data, and surface patterns — but ownership must stay human.

In QA, accountability matters. AI cannot be accountable.

How to Reduce Hallucination Risk in Your QA Process

Reducing hallucinations isn’t about banning AI. It’s about constraining it.

Practical steps:

• Prefer tools grounded in real browser execution
• Avoid black-box “AI insights” without inspectable data
• Keep test results replayable and observable
• Make verification part of the workflow, not an afterthought
• Use retrieval-augmented generation (RAG) and external data sources to ground AI outputs in trusted knowledge bases, reducing the risk of hallucinations and inaccuracies
• Fine-tune models on curated datasets and use high-quality training data to mitigate hallucinations, especially in high-risk use cases

The more a tool shows what actually happened, the less room there is for hallucination. Methods such as Retrieval-Augmented Generation and Human-in-the-Loop validation can further improve AI accuracy.

The term 'hallucination' in artificial intelligence draws an analogy with human psychology, where it typically involves false percepts. The term 'AI hallucination' has gained wider recognition during the AI boom, especially with the rollout of chatbots based on large language models. AI hallucinations can take different forms—such as factual inaccuracies, fabricated citations, and imaginary details—and have significant consequences in various sectors like healthcare, education, media, finance, and entertainment, including the spread of misinformation and incorrect medical diagnoses. These issues can undermine trust in AI systems, particularly in fields like healthcare and legal services where accuracy is critical.

Where BugBug Fits: Reducing Hallucinations by Design

One reason hallucinations are so tempting is that many AI tools operate on abstraction — inferred behavior instead of executed behavior.

BugBug takes a different approach:

• Tests run in Chromium
• Interactions are based on actual DOM state
• Results are visible, replayable, and debuggable

This doesn’t eliminate AI risk entirely — nothing does — but it reduces the surface area where hallucinations can hide. Deterministic execution acts as a natural guardrail.

BugBug isn’t an oracle. It’s a control layer that keeps testing grounded in evidence.

Happy (automated) testing!