Exploring Patterns in LLM Integration

Abstract

The surge in the integration of Large Language Models (LLMs) into software applications marks an evolution in the software development industry, leading to research into architectural frameworks and design patterns suitable for these technologies. This study aims to analyze the implementation of LLMs in software applications, exploring the varying architectures and design patterns that enhance the quality of these integrations. Despite the widespread adoption of LLMs, as highlighted by rapid user growth and diverse applications ranging from chatbots to complex problem-solving tools, a gap remains in the systematic exploration of architectural strategies. This research aims to bridge this gap by analyzing existing LLM applications, identifying architectural patterns and traces of cognitive architectures, and examining how these can be adapted for application development. Through a combination of literature review and structural analysis, this study seeks to offer insights into the architectural underpinnings that support successful LLM integration, thereby contributing to the broader discourse on software architecture in the age of advanced artificial intelligence technologies. With this we provide a library of design patterns tailored to different use-cases of LLM integration in applications.