GPT CoPilot for Sports Betting Platform

Background

Client is a prominent betting platform operating in over 30 global markets, with more than 100 million monthly users. As a regional leader in online gaming, client sought to enhance its user experience by integrating OpenAI’s large language model (LLM) through the GPT CoPilot project. The goal was to revolutionize the way users interacted with the platform and provide a unique betting experience.

Problem

Client faced the challenge of improving user engagement and simplifying the user interface of their platform. They wanted to replace complex and non-intuitive graphical user interfaces with a more natural language-based interaction. The integration of OpenAI’s LLM posed three primary challenges: integration-related challenges, data-oriented challenges, and prompt engineering challenges.

Solution

To address the integration-related challenges, a dedicated team of AI and web technology experts was formed. The interdisciplinary team worked closely to seamlessly integrate the LLM into the Sports Betting platform. For data-oriented challenges, the team employed various data operations to enrich the LLM context, enabling more specific and personalized responses based on user data. Additionally, a prompt versioning system was developed to track and optimize prompts for more accurate and suitable responses.

Results

The integration of GPT CoPilot into the client’s platform yielded significant outcomes. Users experienced a transition from traditional web applications to LLM-oriented applications, which resulted in faster and wider information retrieval. The natural language interface provided a more intuitive user experience, leading to increased customer satisfaction and retention. With the LLM-based chatbot, client witnessed reduced pressure on customer support while ensuring 24/7 availability. The simplified and engaging user interface attracted a greater volume of web traffic and contributed to the growth of new monthly users.

Smart Tip

When integrating LLMs, consider customizing the model to the specific data and needs of your users. Utilize user data to guide LLM behavior and tailor responses accordingly. Better data quality leads to improved chatbot performance and greater user satisfaction.

Smart Fact

OpenAI’s LLM is an incredibly complex software project, with billions and trillions of model parameters and a state-of-the-art architecture. The sensitivity of LLM to user inputs is best exemplified through prompt engineering, where factors like word order, sentence structure, punctuation, and slang phrases influence the model’s output.

Technologies Used

Python, OpenAI Python library, LangChain Python library for LLM context enrichment, FastAPI Python framework for integration and user interaction.