Generative AI Toolkit -- a framework for increasing the quality of LLM-based applications over their whole life cycle

TL;DR

This paper introduces the Generative AI Toolkit, an automated framework designed to enhance the development, deployment, and maintenance of LLM-based applications, thereby improving quality and reducing release cycles.

Contribution

The paper presents a comprehensive toolkit that automates workflows across the entire life cycle of LLM applications, a significant advancement over manual, trial-and-error methods.

Findings

Toolkit improves application quality and stability.

Reduces time-to-release for LLM applications.

Open source availability encourages community adoption and improvement.

Abstract

As LLM-based applications reach millions of customers, ensuring their scalability and continuous quality improvement is critical for success. However, the current workflows for developing, maintaining, and operating (DevOps) these applications are predominantly manual, slow, and based on trial-and-error. With this paper we introduce the Generative AI Toolkit, which automates essential workflows over the whole life cycle of LLM-based applications. The toolkit helps to configure, test, continuously monitor and optimize Generative AI applications such as agents, thus significantly improving quality while shortening release cycles. We showcase the effectiveness of our toolkit on representative use cases, share best practices, and outline future enhancements. Since we are convinced that our Generative AI Toolkit is helpful for other teams, we are open sourcing it on and hope that others will use, forward, adapt and improve