It feels like AI-based tools are making impressive strides with each passing day, allowing businesses to innovate their infrastructure like never before. ChatGPT broke into the mainstream following its launch in 2022, bringing new awareness into the power of dynamic AI-powered systems including versatile chatbots, LLMs, and much more. And as we continue to innovate and push the boundaries of possibility, the CX industry has leveraged this exciting new technology to rewrite how businesses and customers interact.
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Large language models (LLMs) are a hot topic at the moment, for both their high and low points. While LLMs can generate fairly convincing text and images, for example, there are also many risks that can arise. Though the news is filled with success stories, documenting how companies are delivering exceptional CX with LLMs, there are also cautionary tales of how LLMs can irreparably damage your brand reputation and expose your customers to harmful misinformation and biases.
Additionally, LLMs rely on massive amounts of training data to function, and not every business has the resources or bandwidth to manage this type of system effectively. But that doesn’t mean that there aren’t ways for these smaller organizations to participate in the AI-based CX revolution. As LLMs continue to evolve and become more sophisticated, small language models (SLMs) offer an alternative to their larger counterparts.
What are Small Language Models (SLMs)?
Generally speaking, small language models (SLMs) are a streamlined version of LLMs. Whereas LLMs require massive amounts of data to reach their potential capabilities and have billions (or even trillions) of parameters, SLMs have significantly fewer parameters and require much less training data to perform.
Because of their smaller size compared to LLMs, SLMs are generally more efficient and accessible, especially for businesses with limited resources to train, test, and optimize their AI-powered tools. While SLMs still require a lot of training data to succeed, they are much smaller compared to LLMs. This makes it possible for teams to leverage the capabilities of language models, without making a massive investment of time, money, and resources upfront to build the proper infrastructure.
However, there is a trade-off to choosing SLMs as opposed to LLMs. LLMs use such a large amount of data to expand their understanding of human intent and context, allowing the system to generate human-like responses. Though SLMs can still complete specific language-based tasks, they may struggle to handle more complex tasks that require a higher degree of knowledge and understanding.
Notably, SLMs aren’t without their fair share of reputational and financial risks, as well. Just as LLMs can experience hallucinations that can cause them to generate inaccurate, biased, or harmful information, the same applies for SLMs. That’s why a conversational AI optimization solution is a must for any organization looking to make the jump to AI-based CX.
The Need for Conversational AI Testing and Monitoring
When it comes to implementing AI-powered systems into your CX strategy, there are many issues that can emerge during development and in the live environment. And it’s up to you to identify and eliminate these issues before disaster strikes.
For example, your healthcare brand can use a small language model to power a customer service chatbot that can help your patients schedule an appointment, inquire about your hours, or escalate more sophisticated queries to a human agent. However, the chatbot experiences a defect that causes it to schedule several appointments for the same time slot. So, when several patients arrive for their appointments, your staff must either rush through treatments, ask the patients to reschedule their appointments to another date, or have your patients wait for an extended period of time until your doctors can see them all. This situation leads to increased frustration for your patients, and they are unlikely to return for another visit in the future.
This is just one example of how a single CX issue can throw a wrench into your offerings. While the chatbot was designed to create more efficient interactions and cut out needless conversations, it hurt your brand reputation and revenue.
Regardless of whether you choose to use an LLM or a SLM for your CX, it’s important to test, monitor, and optimize your systems throughout every step of the development lifecycle. Businesses that fail to invest in a chatbot testing solution are much more likely to encounter issues, face penalties, and drive their customers away.
Optimize Your Bots for Success with Cyara
Innovative AI-powered CX channels help you deliver quality interactions like never before. With the right solution in place, your business can test, monitor, and optimize bots—such as small language models—that can cut costs, increase productivity, and delight your customers. But you can’t handle this task on your own.
Cyara’s AI-Powered CX Transformation Platform helps you overcome risks that can emerge during the bot development lifecycle. AI-based chatbots and voicebots allow you to better communicate with your customers, and you can’t let risks get in your way. Cyara Botium is the only automated chatbot quality assurance solution, which helps you maximize the value of your investment into AI-based CX channels.
Whether you choose to use large or small language models, there are plenty of risks that can stand in the way of your business’ long-term success. But when you choose Cyara, you can be confident that your systems are always meeting the quality, security, and performance standards that your stakeholders and customers expect.
Contact us today to schedule a personalized demo or visit cyara.com to learn why leading brands trust Cyara to assure CX performance across all channels and platforms, including conversational and generative AI.
