What are large language models? A complete LLM guide

Large language models (LLM) power a growing number of AI tools, including ChatGPT, Bard, and AI agents, which are the next generation of customer service bots. Now, chatbots powered by LLMs are more than mere hype—they’re essential for keeping pace as AI advances.

According to Fortune Business Insights, the gen AI market is valued at $43.8 billion, with CX automation among its most promising use cases. Many forward-thinking companies have begun integrating LLMs into their customer support as the technology’s use cases become more sophisticated. Discover the advantages and challenges of large language models, their common use cases, and the future of LLMs and customer service in our guide.

More in this guide:

• What are LLMs?
• How do large language models work?
• Advantages and challenges of LLMs
• Large language model bot use cases
• How are LLMs trained?
• The future of large language models
• Frequently asked questions
• Build your own LLM-based bot in minutes

What are LLMs?

Large language models (LLM) are machine learning algorithms that can recognize, predict, and generate an output based on training from extensive text-based data sets. This AI technology generates unprecedentedly natural, human-like conversations, enhancing service interactions and customer experiences (CX).

For example, Zendesk AI agents are trained on OpenAI’s LLM models and billions of real customer interactions, enabling them to autonomously resolve complex customer requests and reply like your human agents would.

How do large language models work?

Large language models, such as GPT-4, are trained on vast amounts of text data from diverse sources, enabling them to learn the patterns, structures, and nuances of language. Once trained, these models use their learned knowledge to generate text, answer questions, and perform various language-related tasks.

LLMs work by predicting the next word in a sequence based on the context provided by the previous words. This capability allows them to produce coherent and contextually relevant responses. Often containing billions of parameters, these models can generate high-quality text that closely mimics human writing on a wide range of topics.

Bots pre-trained on LLM models, such as AI agents, can start automating without technical know-how. These bots use deep learning rather than machine learning to predict response accuracy and converse using their training. LLM-powered bots also use conversational AI and a conversational interface to accurately respond to simple and complex interactions.

For example, Zendesk AI agents automatically understand the nuances of CX from day one. This fundamentally differs from traditional AI-powered chatbots, which struggle to resolve complex customer requests and sound more like bots than humans, which can cause customer frustration.

Advantages and challenges of LLMs

With every new technology comes benefits and pain points. With the right guardrails and security certifications in place, the benefits of AI and LLMs outweigh the challenges.

The benefits of LMMs are virtually endless, and they have applications across industries. Here are a few ways they are transforming CX:

• AI agents: Businesses can automatically and autonomously respond to all customer interactions, no matter the time of day or how complex a request is.
• Knowledge base creation: Using Zendesk generative AI tools and our proprietary system, teams can identify missing support articles and use LLM-powered tools to instantly populate AI-powered knowledge base articles from basic information.
• Agent assistance tools: Tools that leverage OpenAI’s LLMs help agents create macros, summarize tickets, make personal recommendations, expand reply options, and more.
• Voice tools: AI solutions trained on large language models enable teams to automatically summarize calls and generate transcripts.

To truly leverage the benefits of large language models, it’s crucial to choose the right AI provider. Keep these considerations in mind when selecting a solution to help you navigate common challenges and avoid potential pitfalls:

• Security: LLMs can be a security risk when not deployed or managed properly. With Advanced Customer Data Privacy and Protection with Zendesk, you can automate CX without compromising data integrity and security.
• Hallucinations: Because LLM-based bots can’t interpret human meaning, they may produce inaccurate responses. Guarantee your AI provider can set parameters and choose a bot designed for your specific use case. For example, AI agents are purpose-built for CX, so they can provide accurate responses to complex questions, unlike other options.
• Time to value: Deploying an LLM-powered bot requires a transformer, hardware, software, and expertise. Invest in a solution like Zendesk to take your deployment from days to minutes without needing a technical team to implement and launch LLM technology.

While it can’t completely resolve every challenge, various strategies and solutions exist to mitigate these pain points, improving LLM-powered bots and CX.

Large language model bot use cases

The elevated capabilities of LLM-powered chatbots—like speaking conversationally without training and adapting to brand tone and voice—translate well to customer support, where quality conversational experiences are critical to CX. According to our Zendesk Customer Experience Trends Report 2024, 72 percent of CX leaders believe the bots they employ should be an extension of their brand’s identity, reflecting its values and voice.

It’s no small wonder, then, how many organizations are jumping on the LLM-powered bot bandwagon. Beyond merely trying to keep up with the Joneses, however, there are plenty of good reasons and effective use cases for implementing AI in customer service and automating support with the help of LLMs:

• Scale your support while lowering the cost per interaction. Intelligent, LLM-powered bots allow teams to process and answer more support tickets and handle unexpected service surges without increasing headcount or straining budget or staffing resources.
• Build a bot in minutes. By connecting your knowledge base to a pre-trained generative AI solution like Zendesk, your bot can immediately start helping customers naturally and conversationally with little to no technical training required.
• Offer customers 24/7 support. Bots don’t need to sleep, eat, or take vacations. They can provide round-the-clock customer care and instant resolutions for queries such as checking the status of an order, requesting transaction info, or changing a password. Autonomous bots like AI agents can even resolve more complex interactions from start to finish.
• Provide multilingual support. LLM-based bots can instantly translate the contents of your knowledge base and converse with native-level proficiency, providing the same level of customer service in all interactions—no matter your customer’s preferred language. They do this without sifting through FAQ pages or producing disjointed, confusing dialogue like an earlier-generation chatbot might.
• Make your human agents’ jobs easier (and more rewarding). Automation elevates the role of your human support agents into AI managers and editors. With mundane tasks removed, they have more time and bandwidth to handle tasks that require empathy, creative problem-solving, and responsible management. This reduces employee turnover, opens up space for upskilling, and powers more rewarding careers.

While this is not an exhaustive list of LLM use cases, these are the most common and rewarding ways AI and automation can be implemented.

How are LLMs trained?

Large language models can have billions of parameters with nodes, layers, weights, and biases, making one model. Using a vast set of data, LLMs use self-learning techniques to predict the next token in a sequence. If the prediction is incorrect, the model adjusts the parameter until the token is correct.

There are two common learning models:

• Zero-shot learning: Base LLMs respond to requests without specific examples. This type of learning typically leads to varied response accuracy.
• Few-shot learning: LLMs receive a few examples, which allow them to improve their responses in specific situations based on new data.

Built-out LLMs can be adapted to perform multiple tasks simultaneously. This type of adaptive learning, known as fine-tuning, is an extension of few-shot learning that continues throughout the model’s use.

The future of large language models

While it may not have the same ring as LLMs, we expect “reasonably-sized” language models will overtake the current model in the years to come. Using generative AI, these language models can run on tens of billions of parameters instead of hundreds of billions. Medium and small language models will be cheaper to run, making it easier to see and reap the benefits of CX automation.

As we look to the future, we also anticipate:

• The verticalization of language models due to specialization
• Industry-specific training to increase the accuracy and efficiency of bots
• Audiovisual training to accelerate model development
• The continued sophistication of AI assistants, including Alexa and Siri

LLMs are constantly evolving in size and ability, so it’s crucial for teams using this technology to understand their current and future capabilities.

Frequently asked questions

Build your own LLM-based bot in minutes

Large language models and AI chatbots are complicated to use, but with Zendesk AI, we make it easy. Our solution is pre-trained on large language models and over 18 million real customer service interactions. As a result, it automatically understands customer needs and is fast to set up without any technical expertise.

With Zendesk AI, you’re ready to go from day one. With the technical barrier to automation lower than ever, anyone can start automating up to 80 percent of interactions with this cutting-edge technology.

Offer bot-supported customer service without compromising quality, time to value, or accuracy by investing in Zendesk AI agents today.