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Article week 20 – 2024

Using Proprietary Large Language Models (LLMs) in Customer Service

With the release of ChatGPT to the public in 2022, companies worldwide have discovered new perspectives in the application of artificial intelligence. Despite the widespread availability and versatile applications of such generic models, there are specific advantages in the development and use of proprietary Large Language Models (LLMs). This article delves into why custom LLMs are essential for businesses and highlights key requirements and providers.

What are Large Language Models?

Large Language Models are algorithms capable of understanding and generating human language in highly complex ways. Their relevance is underscored by their extensive capabilities, especially in customer service – from answering simple customer inquiries to composing comprehensive content. By simulating nuanced human language, LLMs can be used in a variety of applications to develop natural and effective communication strategies tailored to customer needs.

When and why are Proprietary LLMs Useful?

Standard LLMs often fail to meet the specific needs of individual industries or companies. An example of such specific requirements is the exclusive use of proprietary data to generate specific answers or documents that can only be accurately answered with „internal knowledge.“ Examples of such data include the wording of frequently asked questions (FAQs), detailed descriptions of company processes, or pre-formulated responses in customer service that serve as the basis for training the LLM. Using these proprietary contents ensures that the model is precisely tuned to the company’s specific needs and terminology. Additionally, integrating proprietary data allows for continuous improvement and adaptation of the model based on real-time feedback and interactions, which would not be possible with a generic model.

  • Adaptation to Company Language

Proprietary data plays a central role in adapting the LLM to the specific company language. By training with internal data, the model learns to use the exact language, tone, and terminology used in the company’s daily communication. This high degree of personalization significantly increases customer satisfaction, as the communication becomes much more relevant and engaging for the end-user.

  • Security and Data Privacy

Data security and privacy protection are of utmost importance, especially concerning compliance and the integrity of customer information. A proprietary LLM operated on the provider’s separate servers or within the company’s IT infrastructure ensures that sensitive data and information do not leak outside. Unlike general LLMs, where publicly posted questions and data can potentially be used to train other models, a proprietary model keeps everything in a controlled, closed environment. This not only prevents unwanted data flow to the outside but also ensures that all generated content fully complies with internal guidelines and legal requirements.

Providers of LLM Solutions in Closed Environments

The best-known providers of LLM solutions in closed environments include OpenAI, Google Cloud AI, Microsoft Azure AI, and IBM Watson. However, other providers are also available on the market, offering specialized solutions. These providers understand the importance of security, compliance, and tailored communication, and offer companies the necessary tools and frameworks to develop their own LLMs tailored to their specific needs.

Developing a proprietary LLM not only allows companies to optimize their customer communication but also ensures that all data and interactions remain within their own security and compliance frameworks. This maximizes the efficiency and effectiveness of customer service and fosters sustainable customer loyalty through improved and personalized communication.

Carme Prats – Manager

 junokai

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