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The recent proliferation of generative AI (GenAI) models has sparked a lot of interest and excitement. These advanced large language models (LLMs) can do many extraordinary things — from crafting email responses to recommending a fitness routine to generating computer code; the list is seemingly endless.

However, these models aren't infallible. In fact, their propensity to sometimes hallucinate — a phenomenon where they provide responses that incorporate fabricated data that appears authentic — could significantly impact user trust. A recent TELUS International survey found that 61% of respondents were concerned about generative AI increasing the spread of inaccurate information online. 

Understanding what hallucinations are, why they happen and how to prevent them is critical to maintain users' trust in GenAI applications.

Introduction to generative AI hallucinations

A hallucination describes a model output that is either nonsensical or outright false. An example is asking a generative AI application for five examples of bicycle models that will fit in the back of your specific make of sport utility vehicle. If only three models exist, the GenAI application may still provide five — two of which are entirely fabricated. While this is a relatively low-stakes example of a hallucination, some could have far greater consequences. 

Recently a New York attorney representing a client's injury claim relied on a conversational chatbot to conduct his legal research. The federal judge overseeing the suit noted that six of the precedents quoted in his brief were bogus. It turns out that not only did the chatbot make them up, it even stipulated they were available in major legal databases.

While guardrails to prevent GenAI hallucinations need to be put in place, the first step is understanding how, or rather why, these inaccuracies happen.

Why do generative AI hallucinations occur?

While leading AI experts aren't entirely sure what causes hallucinations, there are several factors that are often cited as triggers.

First, hallucinations can occur if the training data used to develop the model is insufficient or includes large gaps leading to edge cases that are unfamiliar to the model. For example, in drafting a contract, a generative model that was fine-tuned using contracts from the financial services industry may not have sufficient exposure to terms and concepts in healthcare law to draft contracts for that industry. Most of the time, generative AI's primary goal is to produce an output in response to a prompt. Even if it doesn't "understand" the prompt, it might craft a response based on insufficient training data, thereby leading to a faulty result.

Similarly, some machine learning (ML) models suffer from overfitting, where the output is accurate for its training data, but inaccurate for new data. The goal of ML model training is to generalize from training instances so that the model can correctly operate on instances it hasn't seen before. Overfitting occurs when a model is trained to the point where it effectively memorizes the inputs and appropriate outputs for the training set, leaving it unable to generalize effectively to new data. For example, a model used to approve loan applications may be considered to be 90% accurate in predicting the likelihood of loan default. However, if it has been subject to overfitting, it likely has an accuracy rate closer to 70%. Therefore, applying the model to future loan decisions will result in a decrease in business profits and an increase in dissatisfied customers.

Another model development challenge is ensuring training texts and prompts are properly encoded. LLMs map terms to a set of numbers — a process known as a vector encoding — and these encodings have some key advantages over working with words directly. For example, words with multiple meanings can have one encoding per meaning, reducing the chance of ambiguity. So, the word "bank" would use one vector representation when referring to a financial institution and another when referring to the shore of a river. Vector representations also allow mapping semantic operations — like finding a similar word — into mathematical operations. Problems with encoding and decoding between text and representations can lead to hallucinations.

Best practices to prevent generative AI hallucinations

While generative AI hallucinations are a concerning issue, there are ways to reduce their frequency and intensity. Consider the following best practices.

The most obvious protection from hallucinations is high-quality input data. It's critical that your GenAI model is trained on a diverse and representative dataset that covers a wide range of real-world examples. Using sufficient, unbiased data is key to training an accurate, resilient model.

Another crucial part of the training process is incorporating human feedback, also known as reinforcement learning from human feedback (RLHF). Because AI models lack a nuanced understanding of language, they can produce responses that are out of context or irrelevant. RLHF provides models with regular corrections via feedback from people, so that an understanding of the world that only a human can have is incorporated into the training process.

Transparency in AI ensures that it's possible to understand a model's inner workings and how it makes decisions. The issue is coming to the forefront as LLMs evolve, particularly because more advanced models are harder to understand.

Users and machine learning professionals should be able to evaluate how a model was trained and review for potential weaknesses in the learning process. Transparency also makes it easier to identify and correct errors that can lead to hallucinations.

A malicious user of a generative AI system could experiment and probe a model looking for ways to make it hallucinate. It's necessary to incorporate counter measures to help monitor and control the types of inputs allowed in generative AI systems. These include using adversarial examples in the model's training to improve its ability to respond appropriately and to audit the model's anomaly detection capabilities. A further means is by data sanitization, which involves checking that no malicious inputs are being fed into the model. And finally, security updates and patches can help block external threats to your AI model.

While generative AI hallucinations are a frustrating hurdle that can significantly impact user trust, they aren't insurmountable. By training models using high-quality data, ensuring model transparency and enacting effective quality control, developers can improve their model's resiliency to deliver accurate predictions.

Hallucinations are an obstacle to building user trust in generative AI applications. Learn about the phenomenon, including best practices for prevention.

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On this episode, we discuss how start-ups can deliver exceptional customer experience (CX) with limited resources — and how businesses of all sizes can use these tactics to thrive in a competitive environment.

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The digital space has never held more potential for retailers — or been more competitive.

Businesses are flocking to eCommerce in record numbers, propelling global online sales to exceed $8.1 trillion by 2026, according to Shopify. But it's not all profit. According to Internet Retailing, over the past five years, there's also been a 60% increase in customer acquisition costs, and more than half of retailers surveyed in Commerce Next's 2022 Digital Trends & Investment Priorities report said those costs are a top risk to achieving their sales objectives.

Given the state of the market, it's easy to see why brands are turning to pull marketing — the process of drawing customers to a company's products and services by building and refining a marketable brand — to differentiate themselves from competitors.

Common pull marketing strategies include advertising, social media marketing, search engine optimization (SEO), blog posts and pursuing media coverage. The most essential pull marketing tactic, however, is also the most understated: a great customer experience (CX).

Three reasons why top-notch CX is the best pull marketing tactic for eCommerce brands

While costly targeted advertising campaigns can create short-term wins for brands, the reality is that these types of tactics aren't long-term profit drivers. Investing in a robust eCommerce customer experience strategy, however, is an integral part of building a brand that is capable of retaining customers, offering seamless buying experiences and encouraging word-of-mouth advertising. All of which can provide significant long-term benefits.

Here is a look at why focusing on creating an excellent customer experience is the best marketing tactic for eCommerce brands.

1. A team of all-star CX agents helps to build lasting relationships with customers

According to Shopify's 2023 Future of Commerce Trend report, at least 60% of consumers purchase from specific brands because of excellent customer service in the past.

And when you consider that 44% of consumers surveyed by TELUS International said that nothing — not even price, convenience or if it's a brand they truly like — excuses poor customer service, it is easy to understand why choosing the right people to support your customers can mean the difference between fostering loyalty and having your customers flock to a competitor.

Plus, given that it's up to seven times more expensive to acquire a new customer than to retain a current one, according to American Express, it's smart to fill your CX agent roster with proficient, patient and empathetic individuals with the tech know-how and soft skills to deliver the kind of service that keeps customers coming back.

2. Digital automation tools help ensure a smooth buying experience

Customer experience is more than just after-sales support. Rather, it's about making the full journey — browsing, research, comparison, selection, payment, delivery and, if necessary, return — as friction-free and connected across channels as possible.

Driving customers to your online platforms is one thing, optimizing them to make a sale is another, and it's a practice that can often be overlooked. Being smart about what you automate, and creating solid parameters around when a live agent should step in, can be a difference-maker for brands.

For example, chatbots can be leveraged to handle simple customer queries around the clock, freeing up your support team to address more nuanced requests. Additionally, proactive live chat prompts can positively nudge customers through the sales funnel. The tool has been known to reduce cart abandonment, which represents $18 billion in lost sales revenue every year, according to Shopify.

3. Elite customer support enhances word-of-mouth advertising

Strong brands attract more organic acquisition — and what's a more effective form of organic acquisition than word-of-mouth?

Word-of-mouth is a big part of pull marketing, and these days, it's driven by customer review sites and social media. One of the best ways to strengthen your brand in the eyes of your target audience is by enhancing your CX so customers will sing your praises and ultimately send others your way.

But if positive customer experiences generate good word-of-mouth, the inverse is true when it comes to negative experiences. For this reason, it's important to mitigate common customer support pet peeves, which, according to the aforementioned survey by TELUS International, include long wait times, numerous transfers to different agents and an inability to speak with a human agent at will (further proving that human connection is always key).

Marketing is all about telling your brand story, differentiating your product and inciting action. Customer service isn't so different. Investing in stand-out CX can enhance your existing pull marketing strategy and go a long way toward creating loyal customers for a better bottom line.

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Learn about how we helped a consumer robotics company improve their digital customer experience with agent-facing automation.

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