AI Systems Engineering Patterns – Alex Ewerlöf Notes (blog.alexewerlof.com)

A new article introduces "AI Systems Engineering Patterns," which outlines 30 techniques derived from conventional systems engineering aimed at enhancing AI engineering practices. These patterns are divided into five main parts, each detailing what the technique is, how it functions, its ideal applications, and associated risks. Noteworthy patterns include Templated Prompting, which simplifies user interactions with AI by replacing complex inputs with intuitive UI elements, and Structured JSON Prompting, which allows technical users to express prompts through strict schemas. This resource serves as a guide for senior engineers and technical leaders, bridging the gap between traditional software engineering and the emerging AI landscape. The significance of this article lies in its potential to equip engineers with established methods adapted for AI systems, emphasizing that core engineering principles remain applicable in this rapidly evolving field. It highlights the necessity of adapting existing technical capabilities while incorporating new paradigms such as model interfaces and structured outputs. For instance, the introduction of the Model Context Protocol (MCP) standardizes how AI applications interact with various services, reducing the complexity of integrations. This melding of old and new techniques not only enhances efficiency but also addresses challenges such as security and user experience, marking a significant advancement in AI systems engineering.