If you’ve got your finger on the pulse of AI, you’ve no doubt heard of “hallucinations.”

While you're more likely to see us highlight the benefits of generative AI for customer service on any given day, we also recognize the importance of addressing the challenges that come with cutting-edge technology. Especially when 86% of online users have encountered an AI hallucination firsthand.

But AI hallucinations don’t have to derail your customer service. As AI-powered solutions become increasingly integrated into customer service operations, understanding this phenomenon is the first step to ensuring accurate and trustworthy automation experiences.

Embracing a proactive and holistic approach to preventing, detecting, and correcting hallucinations are your next steps, and in doing this, businesses can unlock the full potential of AI while maintaining the highest standards of accuracy and reliability.

Here’s everything you need to know about AI hallucinations to get there: what they are, their impact on your customer service, and the strategies for detection and prevention.

What are AI hallucinations?

AI hallucinations occur when an AI model generates outputs that are plausible but factually incorrect or inconsistent with the given context. These hallucinations can manifest in various forms, such as:

1. Incorrect information: The AI model may provide inaccurate details about products, policies, or procedures, leading to misinformation and potential customer dissatisfaction.
2. Fabricated content: In some cases, the AI model may generate entirely fabricated content that has no basis in reality, presenting it as factual information.
3. Logical inconsistencies: The AI model's outputs may contain logical flaws or contradictions, undermining the credibility of the information provided.

Causes of AI hallucinations

In the same way human error can be the result of a variety of factors, AI is susceptible to hallucinations for an array of reasons, including (but not limited to) the below:

1. Training data quality: AI models are trained on vast datasets, and if these datasets contain errors, biases, or inconsistencies, the model may learn and reproduce these inaccuracies.
2. Training processes: Incomplete or “set it and forget it” training processes with insufficient fine-tuning can result in a model that’s more prone to hallucinations.
3. Pattern recognition limitations: AI models generate responses based on patterns they recognize in the training data. When presented with a query that doesn’t have a clear pattern in the data, the model might create a plausible-sounding but inaccurate response.
4. Model architecture: Current AI models, while highly advanced, still have limitations in understanding complex contexts, reasoning, and generating coherent outputs, which can lead to hallucinations.
5. Knowledge base inconsistencies: If the knowledge base or information sources used by the AI model contain contradictory or outdated information, the model may generate hallucinations based on these inconsistencies.

Prompt formulation: The way prompts or queries are formulated can influence the AI model's response, and ambiguous or poorly constructed prompts can increase the likelihood of hallucinations.

The impact of AI hallucinations in customer service

In the context of customer service, AI hallucinations can have far-reaching consequences. 62% of customers say they will stop doing business with a company after just one or two negative experiences, so trust us when we say hallucinations are something you want to avoid at all costs.

More specifically, AI hallucinations in customer service can lead to:

1. Customer dissatisfaction: Providing inaccurate or fabricated information can lead to frustration, mistrust, and a negative customer experience.
2. Operational inefficiencies: If AI hallucinations go undetected, they can result in wasted time and resources as customers and support teams attempt to resolve issues based on incorrect information.
3. Reputational damage: Repeated instances of AI hallucinations can erode customer trust in a company's products or services, potentially damaging its reputation and brand image. Just ask Air Canada .

Preventing AI hallucinations

First, let’s address the most commonly asked question we get as an AI-powered automation platform for customer service: “Is it possible to create an AI agent that never hallucinates?”

The short answer is no. But we can get close. There are ways to mitigate and minimize hallucinations, but current AI technology isn’t perfect and can’t ensure this will never happen.

Think about it this way: you can’t ensure that your human agents — trained experts in your product, services, and brand — won’t make mistakes in their jobs from time-to-time. 66% of agents in call centers reported the primary reason they make mistakes on a call is human error. The reason we’re not seeing this covered as much as AI hallucinations, is that AI has a lower acceptable error rate than humans.

But what exactly does a multi-faceted approach to preventing AI hallucinations look like? Here are some ways you can mitigate AI hallucinations:

1. Robust training data curation: Ensure the data used for training and fine-tuning the AI modelling is thoroughly vetted, cleaned, and free from biases and inconsistencies.
2. Model fine-tuning: Continuously fine-tune and update the AI model(s) with high-quality, domain-specific data to improve their understanding of the customer service context and keep the model(s) current.
3. Knowledge base optimization: Regularly review and update the knowledge base to ensure consistency and accuracy, minimizing the potential for contradictory or outdated information.
4. Prompt engineering: Develop clear and unambiguous prompts or queries to guide the AI model towards generating accurate and relevant responses.
5. Retrieval Augmented Generation (RAG): Implement techniques like RAG, which combines retrieval and generation models to enhance the accuracy and grounding of our AI outputs.

Detecting and correcting AI hallucinations

Still, even with preventative measures in place, AI hallucinations can occur. Researchers found that AI hallucinations may happen as infrequently as 3% or as often as 27% depending on the AI model you’re using (and how you’re using it).

If your AI agent has measurement and analysis tools in place, it’s easy to identify hallucinations and quickly correct them. For example, Ada’s AI Agent has Automated Resolution Insights that highlight opportunities to fill in the gaps in the knowledge base or create new actions to improve your Automated Resolution Rate .

Here are some strategies for detecting and correcting AI hallucinations:

1. Manual review: Establish processes for manually reviewing AI-generated responses, particularly in high-stakes or critical scenarios, to identify potential hallucinations.
2. Automated detection tools: Leverage automated tools and techniques, such as language models trained to detect hallucinations, to identify and flag potentially inaccurate or fabricated outputs.
3. Continuous improvement loops: Implement feedback loops that allow for the correction and refinement of our AI models based on identified hallucinations to ensure continuous improvement over time.
4. Human-AI collaboration: Foster collaboration between human agents and AI systems, leveraging the strengths of both to detect and correct hallucinations while providing exceptional customer service. For instance, you can incorporate learnings from resolved conversations with human agents to guide and improve the AI Agent in handling similar situations.

Risk vs reward: The case for an AI agent for customer service

AI hallucinations aren’t an insurmountable obstacle, but rather an opportunity to continuously refine and enhance our AI systems. While AI hallucinations still present a challenge, these strategies can deter and minimize the potential occurrences.

Despite this risk, our AI Agent is still automatically resolving 70% of customer inquiries. By continuously improving our training data, models, and knowledge bases, implementing advanced techniques like RAG, and fostering human-AI collaboration, we’re able to minimize the occurrence of hallucinations and ensure the accuracy and reliability of our AI Agent.

With the ability to learn and grow over time to reduce the risk of hallucinations, much like an employee, an AI agent can go above and beyond expectations.