The client is a leading artificial intelligence research organization dedicated to developing AI technologies that benefit humanity.

The client’s model was ready to evolve from its current capabilities to tackle more complex tasks such as high-level coding and data analysis. The model needed to integrate APIs, plugins, and third-party tools to enhance its ability to analyze, reason, and select the most relevant tool based on user input. The integration had to be seamless to ensure the model could effectively use these tools without compromising performance or accuracy.

To achieve multimodal processing capabilities, the client and Turing built robust integrations of diverse tools, including programming language interpreters, web browsers, APIs, image interpreters, and file systems. The development process involved multiple stages:

• Technology selection: The client and Turing selected a robust and flexible technology stack to support multimodal interactions. This involved evaluating various technologies for their compatibility, scalability, and ease of integration with the existing LLM infrastructure. The chosen stack needed to support a wide range of tools and be adaptable to future advancements in AI and machine learning.
• Tool integration: The client and Turing developed interfaces tailored for LLM interactions while ensuring the inherent functionalities of each tool were maintained. This involved creating seamless connections between the LLM and external tools, allowing for smooth data exchange and interaction. Each tool was integrated to maintain its full functionality, ensuring the LLM could leverage its capabilities effectively.
• Training process: The client and Turing trained the LLM with numerous examples demonstrating effective tool use. This process was carried out in several phases, each focusing on a specific aspect of tool usage. The training involved multiple iterations, gradually increasing the complexity of tasks to ensure the LLM could handle a wide range of scenarios. Different RLHF techniques were employed to fine-tune the model, helping it understand how to use the integrated tools optimally. Extensive compatibility testing was performed to ensure seamless integration, identifying and resolving any issues that could hinder performance.

This comprehensive approach enabled the model to manipulate and generate diverse types of content effectively, significantly expanding its capabilities. By integrating these tools, the LLM was transformed into a versatile assistant capable of performing a wide range of tasks, from coding to data analysis and creative design.

Implementing multimodal functionality transformed the LLM, enhancing its proficiency across a wide range of tasks and transitioning it to an action-oriented model. The project yielded significant quantitative and qualitative improvements, including:

• Automated programming: The model could now write, debug, and optimize code in various programming languages, accelerating software development. This capability allowed developers to quickly prototype and test new ideas, reducing the time to market for new software products.
• Data science: The LLM assisted in preparing, processing, and analyzing data following the CRISP-DM methodology, proving invaluable in data-driven fields. This included data cleaning, transformation, and visualization tasks, enabling data scientists to focus on deriving insights and making informed decisions.
• Creative design: With access to image generators, the model could produce artistic visuals and draft design concepts from textual descriptions. This capability was handy for creative professionals who needed to quickly generate and iterate on design ideas, enhancing productivity and innovation.
• Real-time information retrieval: Utilizing web browsers and APIs, the model fetched, summarized, and analyzed current information, keeping users informed. This ensured that users had access to the latest information, whether for research, decision-making, or staying updated on current events.
• Interactive education: The LLM curated personalized learning materials, offered tutoring, and provided feedback on assignments, enhancing educational experiences. This personalized approach to education helped learners achieve their goals more effectively, whether they were students, professionals, or lifelong learners.
• Team scaling: Through Turing, the client scaled its team by 3x within three months to support the evolving project needs through Turing. This rapid scaling was essential to meet the demands of the project and ensure timely delivery of high-quality results.
• Model advancement: The model saw significant advancements in API calls, processing, and tool usage, guided by over 1,000 test cases. These advancements improved the model's overall performance and reliability, making it a more powerful tool for users.
• User adoption: User adoption increased by over 80% in the subsequent two quarters, with significant usage in coding (66%), summarization (52%), and research (45%). This increase in adoption indicated the effectiveness of the enhancements and the added value provided by the integrated tools.