What Are AI Hallucinations?

AI hallucinations occur when machine learning models—especially natural language processing (NLP) models like GPT-4—generate information that sounds plausible but isn’t factually accurate.

For example, when asked about the longest street in Poznań, an AI once correctly identified Dąbrowskiego Street but provided an incorrect length. When prompted again, it suggested a completely different street that wasn’t even in the top ten.

A more alarming case happened in software development. A developer asked AI for popular npm packages for visual testing in Cypress*. The AI confidently listed several packages, some well-known and others obscure. However, upon verification, it turned out that half of the suggested libraries did not exist—they were completely made-up names that sounded convincing.

Now, imagine a developer unknowingly downloading one of these non-existent libraries, only to end up with malicious or broken software. This is where hallucinations move from inconvenient to dangerous.

Why Does AI Hallucinate?

AI hallucinations aren’t random mistakes—they stem from the way generative models are built. Unlike humans, AI doesn’t “understand” the world. It generates responses by predicting the most statistically likely sequence of words based on patterns in its training data.

Here’s why AI hallucinates:

  • Outdated or Incomplete Training Data – AI models learn from vast datasets, but that data may be old, incorrect, or missing key details. If an AI hasn't been updated with the latest npm package releases, it might invent names based on similar patterns.
  • Errors in Training Data – If an AI was trained on unreliable or biased sources, it can replicate those errors.
  • Lack of Context – AI doesn’t always “know” what you're asking. If a prompt is vague or missing details, the model fills in gaps with best guesses—which can sometimes be entirely wrong.
  • Inability to Interpret Nuances Like Sarcasm or Colloquialisms – AI doesn't truly understand language the way humans do, which can lead to misinterpretations.
  • The Nature of Large Language Models (LLMs) – LLMs like GPT-4 generate text based on probabilities, not factual accuracy. Unlike a search engine, they don’t retrieve verified information—they create responses that sound right based on language patterns.
  • User Prompt Errors – Sometimes, the issue isn’t the AI, but how the question is asked. If a developer asks for “the best JavaScript library for AI development” without specifying criteria, the AI may include lesser-known or even nonexistent suggestions.

For IT professionals, these hallucinations aren’t just an inconvenience—they can lead to flawed code, security vulnerabilities, and misinformation in tech projects.

How to Reduce AI Hallucinations?

While AI isn’t perfect, there are ways to minimize hallucinations and ensure more accurate outputs.

Always Verify AI-Generated Information

If AI provides a fact, name, or statistic, double-check it using reliable sources like official documentation, research papers, or professional literature.

Craft Clear, Specific Prompts

Vague prompts often lead to incorrect or misleading answers. Instead of asking:
"What are the best AI libraries?"
 Try:
"What are the top five AI libraries for natural language processing, based on GitHub stars and industry use?"

Cross-Check Responses with Multiple AI Models

Different AI models may provide different answers. If one response seems questionable, compare it with another model or search for human-verified sources.

Use AI as an Assistant, Not a Source of Absolute Truth

AI should be treated as a brainstorming tool, not an authoritative source. It’s useful for generating ideas, summarizing content, and assisting with problem-solving—but humans must verify its accuracy.

New Challenges and Opportunities for IT Professionals

AI hallucinations aren’t just a problem—they’re also driving new job roles and skills in the IT industry.

As companies integrate AI into their workflows, there’s increasing demand for professionals who can analyze, verify, and optimize AI-generated information. This has led to the rise of roles such as:

  • AI Model Auditors – Specialists who evaluate AI outputs for accuracy and bias.
  • Data Quality Engineers – Experts responsible for improving the datasets AI is trained on.
  • Prompt Engineers – Professionals skilled at crafting precise AI queries for better responses.

For developers and testers, the key isn’t just using AI but understanding how to fact-check and correct it. AI tools will continue to evolve, but critical thinking and validation will always be essential skills in IT.

At STX Next, you can develop and grow into such roles. Check out our open positions for Data Engineers and Machine Learning Engineers. Join us and evolve in our global environment with diverse projects, contribute to building AI technology, and collaborate with clients who are open to using AI tools in your daily tasks.

Conclusion

AI hallucinations pose real challenges, but they also create opportunities for professionals who know how to work with AI responsibly. As AI becomes more integrated into software development, research, and business, the ability to evaluate, verify, and optimize AI-generated content will be a valuable skill.

Rather than blindly trusting AI, IT professionals should approach it with a mix of curiosity and skepticism—leveraging its strengths while ensuring accuracy and reliability.

The future of AI isn’t just about automation—it’s about collaborating effectively with intelligent systems while keeping human oversight at the center.

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*Learn how AI integration with Cypress can help you automate visual testing.