Quote
Ghazouani, M. (2026) “The Transition from Prompt Engineering To Intent Engineering A Forecast of Human-AI Interaction Paradigms (2025-2030,” The Ilantic Journal
Abstract
This paper presents an analytical forecast of the evolution of human-AI interaction paradigms, specifically examining the transition from prompt engineering to intent engineering as large language models (LLMs) mature. We define prompt engineering as the deliberate optimization of textual instructions to elicit desired model behaviors, and intent engineering as systems capable of inferring user goals from minimal, naturalistic input. Through analysis of proxy indicators including prompt sensitivity, intent recovery rates, context retention metrics, and autonomy ratios, we argue that prompt engineering represents a temporary compensatory technique necessitated by current limitations in model intent inference capabilities. Drawing on scaling law extrapolations, architectural developments in memory and planning systems, and historical parallels in interface evolution, we project that by 2027-2029, advances in model scale, alignment methodology, and multi-turn reasoning will render explicit prompt optimization largely obsolete for mainstream applications. We explicitly integrate the foundational work "Language Models are Few-Shot Learners" (Brown et al., 2020) as evidence that reduced dependence on task-specific optimization has been a consistent trajectory in LLM development. This analysis acknowledges substantial uncertainty regarding deployment timelines, specialized domains, and safety considerations that may modulate this transition.
Authors : Momen Ghazouani
Publication date : January 23, 2026