Conversational AI-based CX channels such as chatbots and voicebots have the power to completely transform the way brands communicate with their customers. As AI has grown more sophisticated in recent years, increasingly more companies have made the decision to leverage these channels, providing efficient and cost-effective self-service customer interactions.
Cyara Botium empowers businesses to accelerate chatbot development through every stage of the development lifecycle.
Natural Language Processing (NLP) and Large Language Models (LLM) are central to how chatbots understand and respond to customer queries by generating natural language responses to human-supplied questions
However, it can be difficult to distinguish between NLP and LLMs. While both hold integral roles in empowering these computer-customer interactions, each system has a distinct functionality and purpose. When you’re equipped with a better understanding of each system you can begin deploying optimized chatbots that meet your customers’ needs and help you achieve your business goals.
An Overview of NLP and LLMs
Simply put, NLP and LLMs are both responsible for facilitating human-to-machine interactions. While NLP makes it possible for bots to understand the various nuances of human language and extract meaning based on defined rules and structures, LLMs leverage large amounts of data to predict and generate human language, allowing your bots to hold conversations and respond to all manner of customer queries.
While each technology is integral to connecting humans and bots together, and making it possible to hold conversations, they offer distinct functions.
What is NLP?
NLP is used to help conversational AI bots understand the meaning and intentions behind human language by looking at grammar, keywords, and sentence structure. NLP uses various processes to interpret and generate human language, including deep learning models, semantic and sentiment analysis, computational logistics, and more. By gathering this data, the machine can then pull out key information that’s essential to understanding a customer’s intent, then interacting with that customer to simulate a human agent.
What are LLMs?
LLMs, such as GPT, use massive amounts of training data to learn how to predict and create language. As an advanced application of NLP, LLMs can engage in conversations by processing queries, generating human-like text, and predicting potential responses. By identifying patterns in extensive training data—such as books, articles, and more—LLMs can produce conversations that feel engaging and simulate human writing styles, though their understanding is based on learned patterns rather than true comprehension.
Based on your organization’s needs, you can determine the best choice for your bot’s infrastructure. Both LLM and NLP-based systems contain distinct differences, depending on your bot’s required scope and function.
Key Differences Between NLP and LLMs
While NLP has been around for many years, LLMs have been making a splash with the emergence of ChatGPT, for example. So, while it may seem like LLMs can override the necessity of NLP-based systems, the question of what technology you should use goes much deeper than that. While each technology is critical to creating well-functioning bots, differences in scope, ethical concerns, accuracy, and more, set them apart.
NLP and LLMs differ in several key areas, such as the following:
Training Data:
NLP systems are built using clear-cut rules of human language, such as conventional grammar rules. These outline how language should be used and allow NLP systems to identify specific information or parts of speech.
LLMs require massive amounts of training data, often including a range of internet text, to effectively learn. Instead of using rigid blueprints, LLMs identify trends and patterns that can be used later to have open-ended conversations.
Accuracy:
Generally, NLP maintains high accuracy and reliability within specialized contexts but may face difficulties with tasks that require an understanding of generalized context.
LLMs, meanwhile, can accurately produce language, but are at risk of generating inaccurate or biased content depending on its training data.
Scope:
Because NLP systems are trained based on language rules, NLP can be used for tasks such as helping brands understand brand perception and ways to improve customer satisfaction, conducting market research, and analyzing customer feedback.
LLMs are often more suited for diverse tasks that require a deeper understanding of context and generating content, such as managing large-scale customer interactions and responding to more complex queries.
Ethical Concerns:
When using NLP, brands should be aware of any biases within training data and monitor their systems for any consent or privacy concerns.
Likewise, LLMs must be continuously monitored for risks, often related to data usage and security considerations. AI governance policies can be used to proactively address ethical and compliance risks.
Limitations:
NLP systems may encounter issues understanding context and ambiguity, which can lead to misinterpretation of your customers’ queries.
LLMs can also be challenged in navigating nuance depending on the training data, which has the potential to embed biases or generate inaccurate information. In addition, LLMs may pose serious ethical and legal concerns, if not properly managed.
Leverage a Chatbot Optimization Solution
Whichever technology you choose for your chatbots—or a combination of the two—it’s critical to ensure that your chatbots are always optimized and performing as designed. There are many issues that can arise, impacting your overall CX, from even the earliest stages of development.
As this technology continues to advance, it’s more likely for risks to emerge, which can have a lasting impact on your brand identity and customer satisfaction, if not addressed in time. When it comes to AI, there is plenty of room for disaster when defects escape notice.
That’s why Cyara’s Botium is equipped to help you deliver high-quality chatbots and voicebots with confidence. When you use Botium, you can easily determine the best bot technology for your needs, including integration with over 55 chatbot technologies and all major NLP engines, so you can seamlessly test and monitor performance and eliminate defects ahead of customer impact.
Botium also includes NLP Advanced, empowering you to test and analyze your NLP training data, verify your regressions, and identify areas for improvement.
There are several key differences that set LLMs and NLP systems apart. With Botium, you can easily identify the best technology for your infrastructure and begin accelerating your chatbot development lifecycle. Contact us to learn more or visit cyara.com.
