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Unraveling AI Hallucinations: The Truth Behind AI’s Misleading Results

A graphic that says "Unraveling AI Hallucinations: The Truth Behind AI's Misleading Results.

Have you ever used AI-powered tools like Grammarly, ChatGPT, orGoogle Gemini in your day-to-day marketing efforts?

Chances are you’ve explored these tools, or at the very least, heard about them. There’s no getting around it: artificial intelligence has a major presence in digital marketing. And it looks like it’s here to stay.

A graph talking about "How Do You Feel About AI in Marketing?"

So why is it that, according to one survey, 36.7% of marketers are either concerned or somewhat concerned about AI technology and its role in digital marketing?

This might be because despite the many use cases of AI tools, AI hallucinations continue to be a major issue with AI outputs.

Let’s talk about what AI hallucinations are, why they occur, why they are problematic, and how you can leverage AI tools in an efficient, ethical, and helpful way. 

Key Takeaways

  • AI hallucinations are instances where AI generates outputs that are factually incorrect or deviate from the intended input.
  • AI hallucinations include false predictions, false positives, and false negatives, which can mislead users and damage brand reputation.
  • Poor training, data quality, reliance on prediction rather than truth, and inherent design limitations are primary causes of AI hallucinations.
  • AI hallucinations can erode trust and reliability, lead to misleading outputs, and pose significant ethical and legal implications.
  • AI solution providers can improve data quality, enhance model training, ensure AI grounding, and maintain continuous monitoring and human oversight to mitigate risks. As an end user, you can adopt careful strategies and formalized approaches about how you use AI to catch hallucinations and avoid using them. 

What Are AI Hallucinations?

Hallucinations are instances where an artificial intelligence model generates an output that is factually untrue or deviates from the intended input (usually both). 

As you can imagine, the type of misinformation perpetuated by hallucinations is problematic for many reasons.

A few AI hallucination examples include:

  • False Predictions: An AI model might fabricate information, claiming events or details that have no basis in reality. For instance, an AI writing tool might generate blog posts with fabricated statistics or case studies that appear credible but are entirely made up. This could mislead readers and damage a brand’s reputation.
  • False Positives: An AI model may mistakenly identify something as present or true when it isn’t. In medical diagnosis, for example, this could mean a healthy patient is incorrectly labeled as having a disease. In a digital marketing context, an AI-powered social media monitoring tool could flag a neutral or positive comment as being negative.
  • False Negatives: An AI model might fail to detect something that is actually there. An AI content optimization algorithm might, for instance, fail to identify a high-performing piece of content, leading to missed opportunities for promotion and amplification.

AI Hallucination Types

When most digital marketers think of “AI”, their mind immediately goes to tools like ChatGPT or Gemini. But these large language models, or natural language generation (NLG) tools, are just one type of artificial intelligence. 

Other types of AI common in digital marketing include chatbots, machine learning, and predictive analytics. This is important because each type of artificial intelligence poses its own unique set of hallucination risks and may be more or less susceptible to different types of hallucination types.

What are some of the most common hallucination types? Here are three you should be aware of:

  • Visual Hallucinations: Instances where AI generates inaccurate or distorted images.
  • Textual Hallucinations: Examples of AI producing incorrect or nonsensical text.
  • Auditory Hallucinations: Scenarios where AI misinterprets or invents sounds.
An AI-generated image with inaccuracies.


(Look closely at this image of a “digital marketer” and you’ll see that there are nonsensical words written on the computer screens, which classify this as both a visual and textual hallucination. Improper spelling or use of letters is a common hallucination in AI image generation).

What Causes an AI Hallucination?

AI hallucinations (along with other issues like those of biased and inaccurate content) occur because AI is flawed. AI is flawed because of its:

  1. Training Data: The vast internet data used to train generative AI is a mixed bag of accuracy and bias, which models can inadvertently absorb and replicate.
  2. Basis in Prediction, Not Truth: Generative AI that leverages natural language processing excels at predicting what word should come next in a string of words, but its outputs aren’t guaranteed to be factual, leading to potential inaccuracies. Unlike search engines, most generative AI platforms do not actually do any research or source their information.
  3. Design Limitations: Even with perfect data, the inherent design of generative AI can lead to the creation of new, potentially untrue content through novel combinations of patterns.

Why AI Hallucinations Are a Problem

There are an untold number of reasons why AI hallucinations can be problematic, each of them based on the specific context.

The fact is that AI does not always cause hallucinations. It can be an extremely useful tool. But because there is always the possibility that it can produce hallucinations, it is important that the human element remains within digital marketing, and any type of review of AI outputs, really.

This is one of the key reasons why marketers maybe shouldn’t fear the (smart and strategic) use of AI. 

That said, here are just a few examples of reasons why AI hallucinations are bad:

  • Impact on Trust and Credibility: AI hallucinations can erode trust in AI tools and their results. When AI-powered analytics produce incorrect data, marketers may make flawed decisions, squandering resources and missing opportunities. Similarly, AI chatbots providing inaccurate information can harm customer trust and brand reputation.
  • Consequences of Misleading Information: Inaccurate AI outputs can have far-reaching negative consequences. Misleading AI-generated content can misinform customers, while flawed personalization in email campaigns can alienate them, reducing engagement and harming the brand’s image. These issues can range from customer dissatisfaction to ineffective marketing campaigns, all with the potential for a substantial negative impact on the brand. See this example below, where Google sourced a satirical website for an answer to a query:
An instance when Google cited a satirical website as an answer for a query.
  • Ethical and Legal Risks: The stakes are even higher in industries where accurate information is essential for ethical and legal compliance. In digital marketing, AI hallucinations that mishandle personal data can result in privacy breaches, leading to legal action and financial penalties. Moreover, misrepresenting product features or benefits through AI-generated content can mislead consumers, raising ethical concerns and potentially incurring legal consequences.

How to Address AI Hallucinations

AI hallucinations are usually addressed by AI companies through improving data quality, enhancing model training, grounding AI, and continuous monitoring and support.

What is “grounding” in AI? Simply put, grounding is “the ability to connect model output to verifiable sources of information”. In other words, grounding ensures that the outputs generated by AI programs are centered in reality and fact as opposed to what “seems right” based on how the AI model has been trained.

But on the end-user side, there are several ways you can safely and ethically use artificial intelligence within  content marketing or digital marketing. While these AI use categories won’t necessarily remove hallucinations from an AI program’s output, using them strategically may help you avoid using hallucinated content: 

A graphic showing how Digital Marketers are using AI.
  • Creating Whole Content: Generating helpful content with AI can be a huge time-saver. Just remember not to plagiarize and substantiate any claims that aren’t common knowledge with your own research (remember, AI can’t do its own research!). Try generating a “zero draft” with AI that you then edit and revise to better fit your branding, wording, and formatting preferences.
  • Creating Partial Content: Uneasy about creating an entire piece of content with AI? Try creating specific sections of content with it instead. This can include elements of the piece itself, or structural elements like schema markup.
  • Editing: AI-powered editing tools like Hemingway and Grammarly are extremely useful for content editing. While they do not replace a good line editor, they can help you correct glaring grammatical or spelling errors before pushing content live or having to make a redaction.
  • Brainstorming & Research: While it’s not recommended to source information directly from LLMs (large language models)and NLGs (natural language generation) like ChatGPT or Gemini, you can ask these types of AI tools about specific topics to give you an entry-point into the conversation or industry, or even a unique angle with which to cover the topic. You can even ask certain tools like Gemini to find you sources from the web that discuss certain topics to make web research faster or get you started!
  • Outlining: Outlining and structural changes including condensing or expanding content are fantastic use cases for AI tools in digital marketing that have far less risk than others while also creating unquestionable efficiencies.
  • Reporting: Data can be incredibly valuable, but having an overwhelming amount of it can make it challenging to analyze and utilize effectively. Using AI to help understand and organize data for reporting purposes is a fantastic way to save time and energy.

The bottom line? Instead of trying to make AI the end-all and be-all, instead, use it strategically. Use it alongside your expertise, industry acumen, and human intuition. Use it as the sidekick to your hero to help you or your teams save time and streamline your processes. 


What is an AI hallucination?

AI hallucinations are incorrect, nonsensical, or misleading outputs of AI systems, including NLGs and generative AI programs that don’t align with reality, the input data, or both. There are various types of AI hallucinations, including text, image, auditory, or predictive hallucinations.

How does an AI hallucination occur?

There are a wide range of specific reasons why an AI hallucination may occur, from poor data quality to algorithmic flaws to lack of grounding and overfitting.

However, at the core of all these reasons is one overarching one: AI models’ outputs are only as good as their inputs and/or the way they have been designed to handle said inputs.

At the end of the day, AI models can’t reason, research, or fact-check themselves.

What are some examples of AI hallucinations?

  • A chatbot that gives the wrong answer to a customer.
  • A writing tool that offers incorrect or made-up statistics.
  • A predictive analytics tool that incorrectly predicts the demand for a product based on flawed data.
  • An image-generation tool that creates a distorted picture.

What are the implications of AI hallucinations?

From eroding trust and reliability in AI tools as a whole to misleading or dangerous outputs that pose ethical and legal risks, there are several important implications of AI hallucinations to be aware of. Arguably even more important than knowing about this risk, however, is using it to structure your decisions about how you use AI, personally and professionally.

How can AI hallucinations be prevented?

From the end user side at least, they normally can’t. AI tool developers are responsible for taking steps to improve data quality, train models, and ground their AI models, striving for continuous improvement and maintaining human oversight all the while.

What is grounding in AI?

AI grounding refers to the process of ensuring that an artificial intelligence system has a clear and accurate understanding of the real-world concepts and contexts it is designed to operate within, thereby contributing to outputs that are both logical and factual.


AI hallucinations, which can range from harmless quirks to potential sources of misinformation and brand damage, are a reality in digital marketing. Their existence doesn’t necessarily mean we should abandon AI tools altogether, however.

The key is to approach AI with a discerning eye, recognizing its strengths and limitations and never taking the output of any AI model at face value. 
Remember, AI is just a tool, not a replacement for human expertise. When used thoughtfully and in conjunction with human oversight, it can be a game-changer, unlocking new levels of efficiency and creativity.

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Neil Patel