Understanding AI Hallucinations: What They Are and How to Prevent Them

You have probably heard about the Tromsø municipality scandal, where officials used AI to help write a report about closing local schools, only to discover that 11 out of 18 research citations were completely fabricated, with some even crediting renowned Norwegian professors with studies they had never conducted. If you are thinking "how could anyone not double-check their sources?" Well, that is exactly the trap of AI hallucinations. They sound so convincing, so professionally written, that even experienced professionals can be fooled. 

If you have started using AI tools for your business or personal projects, you have probably had your own "wait, what?" moment. Welcome to the wild world of AI hallucinations.

Trust me, understanding them is crucial if you want to harness AI's power without the pitfalls. Let us dive into what AI hallucinations are and discuss actionable strategies to avoid them.

What are AI hallucinations?

Let me break it down in plain terms. AI hallucinations occur when artificial intelligence systems, particularly large language models (LLMs) in chatbots, generates information or responses that are completely false, fabricated or misleading. It is like asking a very confident friend for directions, only to discover they have been making up street names the whole time.

Here is the thing: these are not glitches or errors in the traditional sense. They are actually a natural consequence of how AI learns and generates responses. Think of it as the AI's imagination running wild when it does not have solid facts to work with.

Real-life examples from Norway:

Let me share some examples that made the headlines here in Norway:

• The father who was not a criminal: A Norwegian man asked ChatGPT about himself. Innocent enough, right? The AI confidently declared him as a convicted father who had murdered his two sons, aged 7 and 10,  and that he was sentenced to 21 years in prison. Can you imagine? This was not just a minor mix-up. It was a complete fabrication that could have destroyed someone's reputation. If you read Norwegian, you can read the full story at Aftenposten.
• The school report scandal: If you had not heard about the story in my introduction, here it is. In early 2025, Tromsø municipality in Norway learned a hard lesson about AI hallucinations. They had used generative AI to help write an official report about potentially closing local schools. The problem? The report cited 18 research studies, and 11 of them were completely made up. Some even credited well-known Norwegian researchers with work they had never done. This was not just embarrassing, it was a breach of public trust that could have led to major decisions based on fictional data.
• The Hurtigruten fantasy tours: Even our beloved coastal cruise line, Hurtigruten, has not escaped AI's creative liberties. Generative AI tools have been shown to fabricate plausible-sounding, yet entirely false information, like invented port names (like “Nordfjordvik”), incorrect or missing ports in itineraries, and detailed but wholly fictional descriptions—such as nonexistent Viking festivals or medieval cathedrals at small fishing ports. 

These AI hallucinations happen because the model fills knowledge gaps with pattern-based guesses, often without proper fact-checking. 

Why do AI hallucinations occur?

Understanding why these hallucinations occur is half the battle in preventing them.

The data overload problem

The root of AI hallucinations lies in the data-driven nature of these systems. Here is what most people do not realize: AI models are trained on billions of documents from the internet, books, articles, and other sources. This massive dataset includes:

• Information from all the world's cultures and legal systems
• Historical texts from different eras
• Fiction mixed with facts (yes, your AI has "read" fantasy novels)
• Contradictory information about the same topics

Unlike us, the AI does not "know" what is true. It predicts what sounds right based on patterns it has learned.

Lack of context

Another contributing factor is the lack of contextual awareness. When we ask vague questions, the AI must "guess" what context we are referring to. With limitations on context understanding and semantic accuracy, they sometimes make leaps that leave us scratching our head.

Let us look at a concrete example:

Let us say you ask a simple question like: "What happens if I get caught stealing in a store?"

The answer you get can range from mild reprimands to more extreme punishments like hand amputation (which is practiced in certain countries). Why such extremes? Because you did not specify:

• Which country you are in
• Your age
• The current year (laws change)
• What you were allegedly stealing 

Without context, the AI will draw an answer from its entire knowledge base, including laws from countries with vastly different legal systems.

How to prevent AI hallucinations

Now for the good news: you can dramatically reduce AI hallucinations with some simple steps.

1. Be specific and provide context

Effective communication with AI starts with proper prompting. You need to guide the AI models toward accurate and relevant responses.

The difference between a vague prompt and a specific one is like night and day. Let me show you:

Vague prompt: "What's up with global warming?" 

Specific prompt: "What are the three most significant impacts of climate change on Norwegian coastal communities in 2025, according to recent scientific studies?"

See the difference? The second prompt gives AI clear boundaries and context to work within.

Master the art of the specific prompt

When crafting prompts, here are some suggestions you can use:

• Role assignment: Give the AI model a point of view to respond from. For instance, "you are a market researcher...", or "you are a Norwegian law expert..."
• Be specific: Clearly define your question to narrow down the scope of the response. For instance Include relevant details like location, time period, and situation if relevant.
• Context: Offer background information or context to help the AI understand the environment or framework of your question.
• Desired outcome: Indicate what type of answer you are looking for. Whether it is a factual summary, an analytical explanation, or a creative idea. This guides the AI in producing content aligned with your expectations.
• Desired format: Specify the format if needed, whether it is a list, a paragraph, or bullet points. This way you are tailoring the structure of the response according to your needs.
• Constraints: Mention any constraints or boundaries, such as word count limits or exclusion of certain topics.
• Limit the number of questions: Keep the number of questions or requests within a prompt manageable to maintain clarity and focus. Generally, one or two related questions are ideal to avoid overwhelming the AI.
• Iterate and refine: If the responses are not optimal, refine your prompt. 
  

2. Trust, but verify

Always check the AI-generated content against its sources. Are they reliable? Treat it as a starting point rather than the final answer.

• Double-check factual claims, especially numbers and dates
• Be extra cautious with medical, legal, or financial advice
• Cross-reference with official sources
• When in doubt, ask the AI to cite its sources (and then check if those sources actually exist).

3. Feed it your truth

One of the most effective strategies is to have the AI reference your own verified data. Instead of letting AI draw fromt its vast, unverified knowledge base:

• Upload relevant documents and files (keeping security in mind)
• Instruct the AI to only use the provided information

4. Make AI ask you questions

Here is a game-changer: encourage AI to ask for clarification. I often start my prompts with "If you need more information to give an accurate answer, please ask me specific questions first."

5. Choose AI systems built to prevent hallucinations

Here is something important to know: not all AI systems are created equal when it comes to preventing hallucinations. The most reliable ones use something called pre-indexed data and chunk-based processing. And yes, this makes a huge difference.

Let me explain it this way: imagine trying to find a specific recipe in a kitchen where ingredients are scattered randomly versus one where everything is labeled and organized in clear sections. That is the difference between standard AI and systems that use pre-indexed data.

When we work with customers who need absolute accuracy, like legal firms or healthcare organizations, we recommend AI solutions that:

• Organize information before processing it (pre-indexing), making it instantly searchable and verifiable
• Break down large documents into manageable chunks instead of trying to digest everything at once
• Reference specific sections of verified data rather than generating answers from scratch

This is exactly why we use Ayfie's AI Index. Instead of letting the AI freestyle with its vast, unverified knowledge base, these systems work with structured, pre-verified data. It is like having a research assistant who only references your approved company documents rather than random internet sources.

The result? Dramatically fewer hallucinations and answers you can actually trust. Because when you are making business decisions or handling sensitive information, "probably correct" is not good enough.

The future of working with AI

AI hallucinations are not a flaw to fear, but reminders to engage thoughtfully with these powerful tools.

The key is finding that sweet spot between leveraging AI's capability and maintaining healthy skepticism.

AI is an incredible tool with a few quirks that can surprise even the most seasoned tech enthusiasts. Understanding and mitigating hallucinations can help us all use AI more effectively, whether you are building applications or using AI for everyday tasks.

Happy (and careful) prompting!