Large Language Models (LLMs) such as GPT have the potential to revolutionize products and internal processes. By leveraging the power of natural language processing (NLP), companies and professionals can use GPT to automate tasks, generate content, and create personalized experiences. Another use case is creating more efficient workflows, reducing manual labor, and improving customer service. With GPT, businesses can create more efficient and effective products and processes tailored to their customers’ needs. When integrating GPT, it is important to carefully analyze all technical and organizational boundary conditions, you should consider the following nine aspects
- Security: When integrating GPT into internal processes or products, think about the security implications of using a third-party AI system. LLMs are hosted on servers outside of your organization’s control. You should transfer confidential or personal data to third-party AI with great care, either through sufficient anonymization or by limiting the data being processed.
- Performance: This is what you can´t ignore when integrating GPT into internal processes or products. This includes evaluating the accuracy and speed of the system, as well as its ability to scale with increasing data sets.
- Cost: This includes the cost of the system itself, as well as any additional costs that come with training and maintaining the system. For example, processing a 100-page document can cost about one euro when applying the most powerful model, so processing a large internal database of thousands of documents can cause significant costs.Â
- Model alternatives: GPT currently offers four different models with varying sizes, capabilities, and associated costs. Typically, larger models are more performant but slower. Since their preparation took also more time and resources, they are usually also more expensive per query. Thus, it’s essential to consider the trade-offs between performance, speed, and cost when selecting a model.
- Usability: You should optimize the usability of GPT when integrating it into internal processes or products. This involves evaluating the user interface and the ease of use of the system. While the standard chat-based interface is user-friendly and easy to experiment with, businesses can optimize it for specific workflows. For instance, you could import the input automatically from existing data sources while mapping the output to the required target formats.
- Maintenance: This includes regular updates and maintenance of the system. Since the underlying LLMs change frequently, the way they process their input also evolves. Additionally, checking the uptime and performance of the LLM that is used and periodically evaluating if switching to an alternative model or provider is a better option is necessary.
- Property Rights: Using GPT to generate content that violates copyright or other intellectual property rights is a potential concern. Since LLMs are trained on publicly available resources that are subject to copyrights, they often generate texts with a high degree of similarity to those existing sources. This may pose a copyright issue. To mitigate this risk, it is important to consider the rights associated with the output of GPT, based on the form and use of the output.Â
- Trust and validation: When evaluating the output of GPT, context is crucial. For example, using GPT
to generate customer service responses requires ensuring the accuracy and appropriateness of the output and considering the potential legal implications and impact on customer satisfaction and loyalty. Depending on the use case there might be a necessity for additional validation of the output from an LLM. In many scenarios, there are also (semi-)automatic options for this. - Change management: Make sure the organization is prepared for the changes that will come with the new technology. You have to identify potential risks and opportunities associated with the integration of GPT continuously and develop strategies
to address them.
The integration of LLMs can be a challenge in terms of all the aspects above. However, the benefits in terms of increased efficiency and improved user experience can be substantial and will be a worthwhile investment. Effective planning involves assessing the organization’s requirements, identifying potential roadblocks, and determining the best solution to overcome them. This process requires a thorough understanding of the capabilities of LLMs and how you can best exploit them to meet the organization’s goals. By taking an agile approach to evaluating the capabilities of LLMs in the context of your business, you can quickly build a working prototype or MVP and test its effectiveness. This will allow you to validate ideas, assess the potential impact of the technology, and make informed decisions about further investment. If you want to have some further inspiration for projects that leverage ML, NLP, and LLM-based technologies you are welcome to explore our reference cases.