Challenges of Developing In-House Language Models - Why Established Services are More Practical

While it is theoretically possible for a company to create its own large language model, doing so presents several challenges that make it less practical than using established services like ChatGPT or similar alternatives. Here are some reasons why:

• Resource-intensive: Developing a large language model requires significant computational resources, including specialized hardware such as GPUs or TPUs. This can be expensive and may not be cost-effective for many companies.
• Expertise: Creating and maintaining a large language model requires a team of skilled experts in AI, machine learning, and natural language processing. Recruiting and retaining such talent can be challenging and costly.
• Data collection and processing: A company must acquire and process vast text data from diverse sources to train a language model. This involves technical challenges and potential legal and ethical data privacy and usage concerns.
• Continuous updates and maintenance: Language models must be constantly updated and improved to stay relevant and practical. This requires ongoing investments in resources, expertise, and infrastructure.
• Security and compliance: Developing an extensive language model in-house requires adherence to strict security and compliance standards to protect sensitive data and user information. Ensuring these standards are met can be both time-consuming and costly.

Considering these factors, using an established service like ChatGPT or similar alternatives is often a more practical and cost-effective solution for companies looking to leverage the power of large language models in their contact centers. These services come with the benefits of pre-trained models, continuous updates, and expert support, allowing companies to focus on optimizing their customer experience rather than managing the underlying technology.