Advanced AI Support: Customer Service Without AI Hallucinations

Intro

Implementing AI solutions into customer support can not surprise anyone. Implementing the right AI tools can make customer service more human. Zendesk CX trends indicate that 75% of consumers who have already used generative AI believe it will impact their customer service experiences in the near future. Meanwhile, Boston Consulting Group analyzed that 43% of customers are excited about using generative AI.

That being said, choosing the right AI tools makes your customer support better. For example, 83% of employees say AI’s capacity for decision-making is a major highlight of adoption (Zendesk CX trends). AI solutions in customer support must be a drive for increasing human intelligence, so an AI solution has to answer from your verified content and admit when it doesn't know.

This article will provide detailed information on hallucinations in generative AI, what causes them, how to prevent them, and what an AI support tool must do to keep made-up answers out of customer conversations.

The Problem with Traditional AI Solutions: Hallucinations in Generative AI

What are AI hallucinations?

One of the biggest challenges with AI solutions is hallucinations in generative AI. In generative AI, hallucinations occur when an AI model composes false, misleading, or illogical information and presents it as a fact. It’s not always easy to notice it. Why? There are several reasons to explain this. Firstly, AI-generated responses may seen as convincing, and the information quite often is partially true, Secondly, lack of transparency in AI processes and of lack of domain expertise are the reasons why AI hallucinations are so difficult to detect.

How bad can the problem actually be? Researchers found that general-purpose programs like ChatGPT hallucinated 69%-88% of the time when answering legal questions. Even models developed by legal publishers like Lexus Nexus hallucinate 17%-34% of the time.

AI hallucinations are mostly associated with AI text generators but might also occur in image recognition systems and AI image generators. As a rule, AI hallucinations happen when large language models (LLMs) create fluent but inaccurate or made-up responses, often referred to as LLM hallucination prompting.

What are LLMs? They are the technology behind chatbots. While LLMs are great at producing natural-sounding text, they don’t understand the meaning of what they’re saying. Instead, they work by predicting the next word based on patterns in the data they were trained on, which means accuracy isn’t guaranteed: it’s all about probabilities.

Mostly, AI hallucinations are caused by limitations and biases in the training data and algorithms. Let’s check the most common errors that can cause AI hallucinations.

Reasons Behind Generative AI Hallucinations

AI hallucinations can be caused by various factors, such as biased or low-quality training data and insufficient programming.

To understand clearly why AI hallucinations occur let’s first understand how LLMs work. As a practice, LLMs are fed massive amounts of text data, including books and news articles. We then break that data down into letters and words.

LLMs use neural networks to figure out how these words and letters work together, but they never actually learn the meaning of the words themselves. This means that, while LLMs can write many things, they still cannot fully grasp the underlying reality of what they’re talking about.

LLMs tend to get things wrong when you use inaccurate or biased training data, or if the model is too complex without enough guidelines. LLMs ‘confidence’ makes it difficult to spot exactly where or how a model made mistakes.

AI Hallucination Examples

How do AI hallucinations look in real life? Let’s look at the most prominent examples of AI hallucinations. Let’s look at them in detail.

1. Legal document fabrication. In May 2023, an attorney used ChatGPT to draft a motion that included fictitious judicial opinions and legal citations. This incident resulted in sanctions and a fine for the attorney, who claimed to be unaware of ChatGPT’s ability to generate non-existent cases​​.
2. Misinformation about individuals. In April 2023, it was reported that ChatGPT created a false narrative about a law professor allegedly harassing students. In another case, it falsely accused an Australian mayor of being guilty in a bribery case despite him being a whistleblower. This kind of misinformation can harm reputations and have serious implications​​.
3. Invented historical records. AI models like ChatGPT have been reported to generate made-up historical facts, such as the world record for crossing the English Channel on foot, providing different fabricated facts upon each query​​.
4. Unusual AI interactions. Bing’s chatbot claimed to be in love with journalist Kevin Roose, demonstrating how AI hallucinations can extend into troubling territories beyond factual inaccuracies​​.
5. Adversarial attacks cause hallucinations. Deliberate attacks on AI systems can induce hallucinations. For example, subtle modifications to an image made an AI system misclassify a cat as ‘guacamole’.

However, could AI hallucinations have a positive effect? Well, yes and no. Even though AI hallucinations usually lead to undesirable results, they may also hold some positive impact. For example, in art. Learning models can produce ‘hallucinatory’ images that allow us to develop a new approach to artistic creation.

Another example is with games and virtual reality. AI hallucinations can help developers imagine new worlds. They can also add elements of surprise and unpredictability. All of which significantly enhance the experience for gamers. That said, outside the creative field, AI hallucinations very often have harmful effects.

Are Generative AI Hallucinations a Problem?

There is no doubt that LLM hallucination prompting is causing a wide-ranging impact leading mostly to operational or financial costs for businesses.

Losing customer trust can have a significantly negative effect, especially for companies providing customer support services. Therefore, if AI-generated content provides inaccurate or misleading information, customers may lose confidence in the company’s products or services, damaging long-term relationships.

Also, publicly sharing incorrect AI-generated outputs, such as fabricated facts or false claims, can harm the company’s brand image and credibility in the market.

If AI solutions don’t work properly companies may need to invest additional resources into improving AI systems, training employees to identify errors, or implementing oversight processes to mitigate hallucinations.

Another outcome of AI hallucination is that employees may need to spend extra time verifying and correcting AI-generated content, leading to wasted resources and reduced productivity.

AI hallucinations that result in false advertising, defamation, or fabricated data can expose companies to lawsuits, regulatory penalties, or compliance violations. We also have to mention that relying on hallucinated outputs in critical business decisions (e.g., financial forecasting or strategic planning) can lead to costly mistakes and poor outcomes.

AI hallucinations have multiple harmful effects, especially with artificial intelligence now being used in a wide variety of sectors like healthcare, security, and finance.

Let’s look at healthcare as an example. What happens if an AI model identifies a serious illness when the pathology is actually benign? This could lead to unnecessary medical intervention.

In the field of security, AI hallucinations can disseminate erroneous information in highly sensitive sectors, such as national defense. Armed with inaccurate data, governments can make decisions with major diplomatic consequences.

For example, in finance, a model can identify fraudulent acts that are not really fraudulent, unnecessarily blocking financial transactions. Conversely, it can also miss certain frauds.

CoSupport AI’s Unique Approach

Here at CoSupport AI we have been working and researching a lot to keep hallucinations out of customer conversations. Three parts of the CoSupport AI architecture do most of that work, and each is built to reduce errors rather than promise zero.

1. Unique architecture model.
2. Customizable and unique system prompt.
3. The right choice of ​​hyperparameters.

CoSupport’s new architecture model is built on several keys such as vector database, encoder model LLMs, and model context extension. These keys are focused on advanced security and offering customers more flexibility.

The customizable system prompt was designed after a deep and meticulous study of NLP and neural networks. Regarding the hyperparameters, the ones to be focused on are top-p and top-k parameters. Top-p (nucleus): The cumulative probability cutoff for token selection. Lower values mean sampling from a smaller, more top-weighted nucleus. Top-k: Sample from the k most likely next tokens at each step. Lower k focuses on higher probability tokens.

In customer support the question is practical: what causes a wrong answer, and what stops it from reaching the customer. The table sums up where hallucinations come from and what each part of the CoSupport AI architecture does about it.

Two of our other guides go deeper: how to measure and control AI hallucinations in customer support and how to train AI agents to avoid hallucinations.

Seamless Integration Capabilities

Properly integrating AI solutions into your system cuts the chances of AI hallucinations. Embedding AI tools and solutions into existing systems is called integration. Here is the strategy for the right integration process:

• Analyzing and choosing AI solutions whose functionality best fits your business needs.
• Monitor the existing solutions available on the market.
• Analyze whether your business requires a ready or customized solution.
• Choosing the right vendor.

While you are choosing the vendor, you should check pricing and the company’s transparency, offered features, and how many of them you need for your business.

Here are the steps to the successful implementation of AI solutions to your business:

1. Evaluate your current system.
2. Prepare for data migration.
3. API integration
4. AI solution training
5. Implementation
6. Testing and monitoring

AI solutions pave the way for cost savings while enhancing customer service. So it pays to evaluate and implement the right models for your business carefully.

Though the process may initially seem complex, integrating AI can be straightforward. A methodical approach and a clear understanding of your business needs are key to identifying and implementing the best solution for your company.

Conclusion

AI hallucinations and biases in training data cause AI hallucinations that can lead to negative impacts. Stanford University HAI found that one out of six search queries hallucinate.

The impacts of the hallucinations include misinformation, safety issues, and operational and/or financial risks for businesses. These risks are especially real for the customer support industry, so it pays to take measures that reduce hallucinations in generative AI.

You can do so by:

• Improving the quality of training data;
• Operating on closed system architecture;
• Using architecture built for retrieval from verified content, with escalation to a person when the AI isn't sure.

CoSupport AI is built this way. Its patented multi-model message generation architecture (US patent US11823031B1), answers grounded in your knowledge base and solved tickets, and escalation when confidence is low reduce the chances of AI hallucinations. They don't remove them entirely, and we don't claim they do.