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Large Language Models (LLMs) have revolutionized programming and softwareengineering. AI programming assistants such as GitHub Copilot X enableconversational programming, narrowing the gap between human intent and codegeneration. However, prior literature has identified a key challenge--there isa gap between user's mental model of the system's understanding after asequence of natural language utterances, and the AI system's actualunderstanding. To address this, we introduce Programming with Representations(PwR), an approach that uses representations to convey the system'sunderstanding back to the user in natural language. We conducted an in-labtask-centered study with 14 users of varying programming proficiency and foundthat representations significantly improve understandability, and instilled asense of agency among our participants. Expert programmers use them forverification, while intermediate programmers benefit from confirmation. Naturallanguage-based development with LLMs, coupled with representations, promises totransform software development, making it more accessible and efficient.

PwR: Exploring the Role of Representations in Conversational Programming