Over the past several years, it often feels like AI-based systems are moving at a breakneck pace. The seemingly overnight success of ChatGPT, for example, completely changed the way that customers are interacting with AI, while—within the CX landscape—more sophisticated models have opened the door to a wide range of contact center innovations with improved efficiency and cost savings.
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Today, natural language processing (NLP) and large language models (LLMs) dominate many conversations as businesses work to understand the best use cases for each type of system and determine how to maximize the value from their investments.
As technology continues to evolve, it’s become clear that NLP has blazed the trail for AI-based technology. While both NLP and LLMs are closely intertwined, they each hold individual strengths in determining how bots generate and understand customer queries. By gaining an understanding of each model, you can identify the best application for AI-powered CX within your organization and take the next step in navigating the future of AI-based interactions.
What is NLP?
Natural language processing, called NLP, is used to help bots understand, interpret, and respond to human language. NLP uses algorithms and other training data to process human language by looking at elements such as grammar, keywords, and sentence structure. During an interaction, your bot can pull out the key information that’s needed to interpret the meaning of a customer query, then simulate an agent’s response.
While traditional NLP models relied on clear-cut language rules to identify specific types of information or parts of speech, modern systems can perform a wider range of tasks including sentiment analysis and language translation.
Use Cases for NLP
NLP models can be used for several different functions across the CX space, including:
- Automating routine, manual tasks
- Redacting sensitive customer information
- Analyzing language used during customer interactions
- Providing actionable, data-driven insights
- Summarizing text
- Performing language translation
By using NLP-based bots, contact centers can significantly improve customer satisfaction with efficient self-service options. These systems have the potential to help you maximize your team’s productivity and improve customer satisfaction rates.
However, NLP models are also unable to generate new content. While they excel in completing well-defined tasks, ambiguity in more complex tasks can lead to harmful misinterpretations.
What are LLMs?
LLMs, such as the widely known ChatGPT, are trained to understand, generate, and manipulate human language. These systems leverage massive amounts of training data to learn how to engage in conversations by generating human-like text and predicting potential responses. Because of their capacity to identify patterns in text, LLM-based bots are able to simulate human writing styles that mimic how an agent might actually interact with a customer.
Whereas NLP models, for example, are incapable of generating new content, LLMs can actively generate highly personalized responses and solutions to a customer’ query.
Use Cases for LLMs
LLMs are trained using a massive amount of data—such as from books or articles—and have the capacity to handle a diverse range of tasks, including:
- Creating content
- Personalizing training and education
- Automated routine tasks
- Interpreting data sets
- Conducting customer sentiment analysis
- Providing localized content and language translations
LLMs are marked by their ability to create new content, though it’s critical to understand that their understanding of language is based on learned patterns rather than true comprehension. This means that LLMs are at risk of generating inaccurate or biased content depending on the training data.
While NLP and LLMs may overlap in providing some similar functions, they each contain distinct differences that set them apart. Understanding the differences between each model and your business’ unique needs is key to identifying which system is best for you.
Navigating the Future of AI-Based CX
Today, technology is continuing to change at a breakneck pace, driving businesses forward with new opportunities to innovate, especially in the contact center ecosystem. But how can you most effectively navigate the future of AI-powered CX and maximize your investment value?
NLP and LLMs: Integrating for Success
As discussed, there are several key differences that set NLP and LLMs apart, with each model containing their own strengths and weaknesses. But by combining LLMs and NLP architecture, you can take a step forward in building a more sophisticated bot, with the capacity to address a wider range of use cases.
Together, NLP and LLM architecture is suited for applications that require a more advanced skill set. The convergence of these models allows for better quality robot-human interactions, with a bot that combines LLM’s generative capabilities with NLP’s language rules.
The Need for Testing
However, it’s critical to understand that no bot is perfect, and there are still many risks that can emerge along the way, regardless of the foundational model you choose. Continuous and thorough testing and monitoring is paramount to identifying potential reputational, financial, and compliance risks before your customers are impacted or your business is subject to costly penalties.
Regular testing is essential to ensure you’re delivering accurate and reliable bots, without running the risk of exposing your brand and customers to harmful biases, misinformation, and other risks. By integrating an automated chatbot testing and monitoring solution, you can be confident that your bots are always performing as intended and that you’re able to catch any potential risks that will impede your ability to serve your customers’ needs.
Optimize Your Bots for Success with Cyara
Already, AI-based CX channels are transforming the ways that businesses are communicating with their customers. As this technology continues to evolve and push the boundaries of what’s possible, an automated conversational AI testing and monitoring solution is essential to set your brand up for success—regardless of the foundational model you’re using for your bots.
Whether you’re using NLP, LLMs, or an integration of the two, there are many risks that can disrupt your CX offerings and negatively impact your customers. But Cyara is here to help.
Cyara Botium is our award-winning conversational AI optimization platform, designed to help you regain control through every stage of your bot development journey and ensure that your systems are always performing as intended. Botium empowers you to select the best bot technology for your needs, maximize efficiency, and execute continuous improvements that will delight your customers.
It’s time to maximize the value of your AI investments and eliminate unnecessary financial and reputational risks. Learn how leading brands leverage Cyara Botium to unlock the full potential of their conversational AI-based systems by visiting cyara.com or contact us to schedule a demo.
