Designing the decision layer: A rich career path for systems-oriented content designers

As AI systems become core to product development, content designers with systems instincts are finding their leverage point by moving upstream: from refining surface-level outputs with product designers to shaping underlying structures directly with machine learning (ML) engineers.

This talk explores one emerging specialization within our evolving discipline, grounded in a real 0-to-1 LLM pipeline project: how content designers who gravitate toward taxonomy, IA, and engineering collaboration can shape the “decision layer” of AI systems rather than its outputs.

Instead of focusing on how to use AI to write microcopy, we’ll examine how content designers can define structured inputs and outputs, build reusable JSON repositories, and create human-in-the-loop evaluation frameworks that influence model behavior — and how to effectively present that case to engineering leadership.

In this session, you’ll learn how to:

• Identify where content designers can influence AI system architecture beneath the surface, including schema design, structured outputs, and evaluation frameworks.
• Apply a practical collaboration framework for working with ML engineers, including how to define structured contracts, separate extraction from ranking and presentation, and design reusable JSON repositories that scale across teams.
• Recognize the language-shaped responsibilities engineers already carry and would readily hand off, and use that understanding to propose and position systems-level content work within your own organization.