AI is revolutionizing the way nearly every industry operates. It’s making us more efficient, more productive, and – when implemented correctly – better at our jobs overall. But as our reliance on this novel technology increases rapidly, we have to remind ourselves of one simple fact: AI is not infallible. Its outputs should not be taken at face value because, just like humans, AI can make mistakes.
We call these mistakes “AI hallucinations.” Such mishaps range anywhere from answering a math problem incorrectly to providing inaccurate information on government policies. In highly regulated industries, hallucinations can lead to costly fines and legal trouble, not to mention dissatisfied customers.
The frequency of AI hallucinations should therefore be cause for concern: it’s estimated that modern large language models (LLMs) hallucinate anywhere from 1% to 30% of the time. This results in hundreds of false answers generated on a daily basis, which means businesses looking to leverage this technology must be painstakingly selective when choosing which tools to implement.
Let’s explore why AI hallucinations happen, what’s at stake, and how we can identify and correct them.
Garbage in, garbage out
Do you remember playing the game “telephone” as a child? How the starting phrase would get warped as it passed from player to player, resulting in a completely different statement by the time it made its way around the circle?
The way AI learns from its inputs is similar. The responses LLMs generate are only as good as the information they’re fed, which means incorrect context can lead to the generation and dissemination of false information. If an AI system is built on data that’s inaccurate, out of date, or biased, then its outputs will reflect that.
As such, an LLM is only as good as its inputs, especially when there’s a lack of human intervention or oversight. As more autonomous AI solutions proliferate, it’s critical that we provide tools with the correct data context to avoid causing hallucinations. We need rigorous training of this data, and/or the ability to guide LLMs in such a way that they respond only from the context they’re provided, rather than pulling information from anywhere on the internet.
Why do hallucinations matter?
For customer-facing businesses, accuracy is everything. If employees are relying on AI for tasks like synthesizing customer data or answering customer queries, they need to trust that the responses such tools generate are accurate.
Otherwise, businesses risk damage to their reputation and customer loyalty. If customers are fed insufficient or false answers by a chatbot, or if they’re left waiting while employees fact-check the chatbot’s outputs, they may take their business elsewhere. People shouldn’t have to worry about whether or not the businesses they interact with are feeding them false information – they want swift and reliable support, which means getting these interactions right is of the utmost importance.
Business leaders must do their due diligence when selecting the right AI tool for their employees. AI is supposed to free up time and energy for staff to focus on higher-value tasks; investing in a chatbot that requires constant human scrutiny defeats the whole purpose of adoption. But are the existence of hallucinations really so prominent or is the term simply over-used to identify with any response we assume to be incorrect?
Combating AI hallucinations
Take into consideration: Dynamic Meaning Theory (DMT), the concept that an understanding between two persons – in this case the user and the AI – are being exchanged. But, the limitations of language and knowledge of the subjects cause a misalignment in the interpretation of the response.
In the case of AI-generated responses, it is possible that the underlying algorithms are not yet fully equipped to accurately interpret or generate text in a way that aligns with the expectations we have as humans. This discrepancy can lead to responses that may seem accurate on the surface but ultimately lack the depth or nuance required for true understanding.
Furthermore, most general-purpose LLMs pull information only from content that’s publicly available on the internet. Enterprise applications of AI perform better when they’re informed by data and policies that are specific to individual industries and businesses. Models can also be improved with direct human feedback – particularly agentic solutions that are designed to respond to tone and syntax.
Such tools should also be stringently tested before they become consumer-facing. This is a critical part of preventing AI hallucinations. The entire flow should be tested using turn-based conversations with the LLM playing the role of a persona. This allows businesses to better assume the general success of conversations with an AI model before releasing it into the world.
It’s essential for both developers and users of AI technology to remain aware of dynamic meaning theory in the responses they receive, as well as the dynamics of the language being used in the input. Remember, context is key. And, as humans, most of our context is understood through unspoken means, whether that be through body language, societal trends — even our tone. As humans, we have the potential to hallucinate in response to questions. But, in our current iteration of AI, our human-to-human understanding isn’t so easily contextualized, so we need to be more critical of the context we provide in writing.
Suffice it to say – not all AI models are created equal. As the technology develops to complete increasingly complex tasks, it’s crucial for businesses eyeing implementation to identify tools that will improve customer interactions and experiences rather than detract from them.
The onus isn’t just on solutions providers to ensure they’ve done everything in their power to minimize the chance for hallucinations to occur. Potential buyers have their role to play too. By prioritizing solutions that are rigorously trained and tested and can learn from proprietary data (instead of anything and everything on the internet), businesses can make the most out of their AI investments to set employees and customers up for success.
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