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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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The words "product-market fit" have adorned tech industry pitch decks for some time. It's a simple concept, really, and something all successful businesses need to prove. To achieve product-market fit, the product(s) you offer as a business must align with the needs of the market. Without such a fit, you might have an idea, but perhaps not a business.

In fact, even if you have product-market fit, and therefore a business, you still might not have a long runway without profitability. This is particularly the case when challenging economic environments make venture capital funding more difficult to come by. That is precisely why scale-ups need to think beyond product-market fit, and strive to achieve profit-market fit.

Harvard Business School senior lecturer Jeffrey Rayport recently described profit-market fit on the HBR IdeaCast podcast. According to Rayport, profit-market fit is achieved when a venture not only shows that it can quickly ramp up revenue and its customer base, but also finds its sweet spot where every new customer brings in additional revenue and incurs only marginal cost. It's at this point, explains Rayport, that a venture "moves towards sustainability."

This major milestone isn't likely to be reached by accident, though. Rather, you need to be deliberate, and deploy strategies to overcome common challenges in the scaling journey.

Many companies, especially those in tech, can be successful in achieving product-market fit, says Rayport during the podcast interview, "But the wheels come off the bus in some way during the scaling phase."

Imagine you head up a start-up that seems destined for unicorn status. You have an app and it's topping the charts. You have customers and your number of active users is going through the roof. To date, your impressive numbers have earned you no shortage of admirers, and by extension, funding. But to get to where you are, you've needed to spend: Your servers, your offices, the growth of your team and a myriad of other costs outstrip the money that your product brings in. As a result, you can't sustain — let alone grow — your business without additional outside funding. Now, facing rising interest rates and economic pressures, that funding looks like it is going to dry up. Put simply, you haven't found profit-market fit — the relationship between the money and the product that enables businesses to steer themselves toward sustainability.

A strong profit-market fit helps you remove internal constraints on your company's growth, and makes you far less dependent on outside funding.

Challenges in establishing profit-market fit

There are, of course, challenges associated with scaling and reaching the point of profit-market fit.

Once there's a good product-market fit, a scaling company must keep up its generated demand and make strides toward product profitability. This brings us back to matters of efficiency — a word that's popping up all across the tech industry and one that's top of mind for start-ups, scale-ups and mainstays alike.  In fact, Meta CEO Mark Zuckerberg even went as far to say, "Our management theme for 2023 is the 'Year of Efficiency,' and we're focused on becoming a stronger and more nimble organization." For tech brands of all sizes, the pursuit of efficiency means keeping costs under control and doubling-down on activities that are generating revenue — but that's not where the work ends.

Other challenges include providing a consistent digital customer experience (CX) as your company grows, and maintaining a coherent organizational culture where people are on the same mission and where their efforts align.

Brian Hannon, global head of customer experience innovation - technology at TELUS International, points to the value of experience when facing challenges in the scaling journey. "Achieving profit-market fit is difficult. It's even harder when you're learning every lesson the hard way," explains Hannon. "You can build up a lot of momentum as a hit tech company, but it's easy to run into real challenges if you don't have a firm grasp on how to scale in a sustainable way."

Outsourcing digital CX to reach profit-market fit

One way to scale with efficiency in mind is to outsource aspects of your digital customer experience.

Keeping your CX in-house means dealing with sudden peaks and dips in demand that quickly become costly on the hiring front, to say nothing of the complexities that come serving new markets and establishing operations in new geographies. What's more, outsourcing can save scale-ups from having to bear the full cost and risk associated with big and potentially transformational technological investments.

You can't overlook the value in an outsourcing partner's experience either, explains Hannon. "They'll have worked with countless companies before, and will be able to provide direction from lessons learned. An outside perspective on where you can introduce greater efficiency can be a real asset as you grow."

Today, conversations about efficiency have a way of leading to automation like robotic process automation (RPA). This form of automation provides around-the-clock capabilities, produces replicable results at scale and handles repetitive tasks so your team members don't have to. These capabilities enable automated service channels — such as chatbots and intelligent FAQs — that play an important role in a modern digital customer experience operation. With the expertise of a proficient outsourcing partner, introducing automation becomes less resource-intensive.

"It can feel safe to stick with what you know. But to make the progression from product-market fit to profit-market fit, brands need to keep an open mind to new technologies, and new ways of bringing efficiency."

Outsourcing with TELUS International for next-generation, digitally led customer experiences means benefiting from 18 years of history and global reach that has served more than 650 global clients in 30 countries of operation. Reach out today to learn more about how we can help your scaling company achieve profit-market fit.

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