• December 13, 2023
  • By Zach Linder, chief operating officer, Stellar

How to Provide Superior Customer Service With LLMs

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Is there anything more frustrating than calling a customer service line, being bounced around from robot to human and back again, and having to explain your issue every time someone new is on the line? Your customers are not a number in line; they are individuals who want to be acknowledged and treated as such. The best way to accomplish that is through personalization.

From marketing to customer service, a majority of consumers expect to receive personalized experiences. Therefore, companies need to have a great deal of data about their customers at the ready. This is important for outbound marketing initiatives to ensure you are connecting with your customers when and how they would like, but it is critical for inbound inquiries if customers need help or have a service issue.

Data is often housed in different departments of a company depending on its nature. Internally, this is important for security, operations, and more. From the outside, though, keeping billing completely separate from IT can look siloed and inefficient rather than secure. This is where AI comes in. Artificial intelligence can pull all of a customer’s information through just a few prompts to connect them with the right representative or department with all of their information aggregated in a convenient dashboard.

This is great for historical information. But what about helping with the customer’s current need? Incorporating machine learning (ML) into AI allows a company’s system to train on existing quantitative data to understand who the customer is and extract potential solutions. But what if we took it one step further to not only extract the perfect solution for the current issue but to predict future needs? Now we must introduce large language models (LLMs).

LLMs teach computers to read and derive meaning from language, not just numbers. With this capability, LLMs can generate new humanlike text to assist with a number of tasks, such as writing and conversation, output validation, information retrieval, and parsing text from unstructured documents. All of these applications and more give the user—in this case, a customer service representative—tools and information for a more natural and effortless interaction with both the technology and the customer.


LLMs can lead to major time and cost savings, particularly at the entry point of a customer service issue. LLMs are conversational and can comprehend and respond to intricate user queries, making them ideal tools to improve the customer experience in a number of ways. LLMs can handle the following tasks:

  • Understand questions and provide answers in any language, removing language barriers, and enabling customer service for a global customer base
  • Simultaneously manage a high volume of customer inquiries to provide quick and accurate responses
  • Expedite training for new customer service representatives and employees
  • Provide customer support anytime and in any time zone
  • Reduce customer waiting times and manage a growing number of customer interactions without compromising response quality

This all leads to good customer service while saving time and money for the company. But to truly bring customer service from good to great, we need to look to the latest iteration of generative AI.

Superior Customer Service

A company’s customer database can house millions of data points that are relevant to an individual consumer. With that data, businesses can generate dozens or hundreds of different customer service scenarios based on existing data. When a customer reaches out for support, whether that is via phone, chatbot, or anything in between, they want a solution, and they want it quickly. If AI is generating hundreds of recommendations, how does the technology—let alone the customer service rep—know which is best?

Generative AI combines LLMs and machine learning to not simply produce results but to refine and learn from them. Many organizations have side-stepped ML in order to implement LLMs more quickly, but one can only thrive with the other. LLMs generate solutions; those that work best are recorded; and then ML learns and adapts to those choices. Rather than generating likely scenarios, it understands why a solution was chosen and why it works.

There is a cost to integrating AI into your business’s systems and deploying it across your tech stack. But the cost of not doing so is far greater. Ninety-two percent of consumers report that a positive customer service experience makes them more likely to make another purchase. Don’t let your competitors beat you to providing hyper-personalized, superior customer interactions.

Zach Linder is the chief operating officer at Stellar. A seasoned data, security, and AI application expert, Linder understands topical AI challenges such as masking and bias mitigation and has comprehensive knowledge of compliance protocols such as HIPAA and GDPR. Linder’s extensive leadership experience includes leading the development of an AI- and text-based recruiting platform and overseeing technology service projects for major enterprises such as Eli Lilly, Johnson and Johnson, and others.

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