Effective Customer Service: Own AI models vs. LLM-based chatbots

Chatbots are playing an increasingly important role in modern customer service. The question of whether to use a chatbot application that uses pre-trained Large Language Models (LLMs) or to develop your own AI model using supervised learning is crucial for the efficiency and cost-effectiveness of customer service. Both methods offer specific advantages and challenges, which are explained below.

LLM-based chatbots

LLM-based chatbots, such as those that use GPT-4 or other models, offer quick implementation and broad language understanding. They can be integrated immediately and offer high accuracy in answering a wide range of customer queries.

Advantages:

• Efficient chatbot solutions are available: There are modern chatbot solutions on the market that are designed to be easily integrated into existing systems using their own manuals and data.
• Fast implementation: Pre-trained LLMs such as GPT-4 or Llama are available and can be made ready for use quickly.
• Comprehensive language capabilities: Such models understand complex queries and provide precise answers.
• Easy integration: The existing APIs and SDKs enable seamless integration into existing systems.

?Disadvantages:

• Cost: The ongoing costs of using an LLM can be significant.
• Dependence on third-party providers: The availability and cost structure of the provider influence the service.
• Limited adaptability: Adaptation to specific business requirements is limited.
• Intelligent search as a key component: Since these applications often use an intelligent search, the answer depends heavily on the quality of the search function before AI (LLM) is used.

?Own AI models

Developing your own AI model requires an initial higher investment in time and resources, but offers significant long-term benefits, especially in terms of adaptability and cost control.

Advantages:

• High adaptability: A customized model can be tailored precisely to the specific needs of the department.
• Long-term cost efficiency: While the initial costs are high, the long-term operating costs can be lower because there are no external usage fees.
• Continuous improvement: By continuously training and updating the model, performance can be constantly optimized.

Disadvantages:

• High initial costs: Data preparation, model training and infrastructure require a significant initial investment.
• Complexity: Developing and maintaining your own model requires specialized knowledge and resources.
• Time required: Creating a powerful model can be time-consuming. However, developing your own chatbot can also be very time-consuming.

?Recommended approach: Hybrid solution

An effective approach could be a hybrid solution:

1. Start with an LLM-based chatbot: First use an LLM-based solution to quickly provide a functioning chatbot application. This makes it possible to answer customer inquiries immediately. But to do this, use chatbots that are already available on the market instead of developing your own application.
2. Parallel construction of your own model: Use existing data to develop an own, customized AI model. This makes it possible to better meet specific requirements and keep control over model costs.
3. Integration and transition: As soon as the own model is sufficiently powerful, you can change step by step from the LLM-based solution to the own model. This can be done by gradually adopting certain categories of requests by the own model, while the LLM continues to serve as a backup.

Conclusion

While LLM-based chatbots offer a fast and flexible solution for customer service, your own AI model can be the better choice in the long term. It enables greater customization and cost efficiency. The hybrid strategy that combines both approaches ensures that you can immediately benefit from the advantages of an LLM while building a customized, cost-effective system for the future.