AI prompts now shape how machines think and decide

Prompts do not simply request information; they structure how models assemble explanations, link events, and simulate reasoning. In this sense, prompts act as cognitive scaffolds, organizing how AI systems generate apparent meaning.

With AI systems becoming embedded across education, governance, science, and creative industries, attention is shifting away from model architecture alone toward the mechanisms that shape how these systems are controlled in practice. One of those mechanisms, prompting, has moved from a niche technical skill to a defining layer of interaction between humans and AI. 

That transformation is examined in the editorial Prompts: the Double-Edged Sword Using AI, published in Frontiers in Artificial Intelligence. The editorial argues that prompting has become a foundational component of modern AI systems, carrying both unprecedented opportunities and significant risks.

Prompting becomes a new programming paradigm

General-purpose foundation models consolidate vast capabilities into single architectures that can be adapted to diverse tasks through prompts rather than task-specific retraining. As a result, natural language has effectively become a high-level programming language for AI systems.

This shift has expanded access to AI capabilities by lowering technical barriers. Users no longer need to write code to influence system behavior; instead, they can guide models through linguistic instructions. However, the editorial argues that this accessibility masks a growing concentration of power. Prompts determine which capabilities are activated, how uncertainty is handled, and which explanations are constructed, making them a critical site of influence over AI outcomes.

The research reviewed in the editorial shows that prompting is increasingly subject to technical optimization. Automated methods, including algorithmic prompt search and evolutionary approaches, can discover prompt formulations that outperform human-designed inputs. While these techniques improve efficiency and performance, they also introduce new forms of opacity. As prompts are optimized by machines rather than humans, the gap between operational effectiveness and human interpretability widens.

This development challenges prevailing assumptions about transparency and accountability. If system behavior is driven by prompts that are difficult for users to understand or reproduce, responsibility becomes harder to assign. The editorial highlights this tension as a defining feature of contemporary AI systems, where control is both democratized and obscured.

Prompts shape knowledge, meaning and understanding

Prompts do not simply request information; they structure how models assemble explanations, link events, and simulate reasoning. In this sense, prompts act as cognitive scaffolds, organizing how AI systems generate apparent meaning.

The authors draw attention to the fact that large language models operate without embodiment or lived experience. Their outputs are grounded in statistical patterns rather than direct interaction with the world. Prompts therefore play a crucial role in bridging the gap between human intention and model behavior. By framing questions, defining context, and constraining responses, prompts guide models toward particular interpretations of reality.

This has profound implications for knowledge production. The editorial argues that prompts influence what models treat as relevant, how causal relationships are expressed, and which perspectives are foregrounded or excluded. In academic research, policy analysis, and educational settings, prompting choices can subtly shape conclusions and narratives, even when outputs appear neutral or authoritative.

The epistemic power of prompting also raises concerns about bias and distortion. Poorly specified prompts can reinforce stereotypes, oversimplify complex issues, or privilege certain viewpoints. Conversely, carefully designed prompts can surface nuance, uncertainty, and alternative perspectives. The editorial stresses that these effects are not incidental but inherent to the role prompting now plays in AI-mediated reasoning.

Ethical responsibility and the dual-use nature of prompting

The authors highlight the dual-use nature of prompting as a defining challenge. On one hand, prompts can be used to impose explicit constraints, guide systems toward responsible behavior, and reduce harmful outputs. On the other, they can be manipulated to circumvent protections, extract sensitive information, or amplify bias.

This duality complicates governance efforts. Traditional AI ethics frameworks often focus on model design or output moderation, but the editorial argues that prompting sits at the intersection of user intent, system capability, and deployment context. Responsibility cannot be located solely with developers or users; it is distributed across sociotechnical systems.

The editorial also connects prompting to emerging discussions of AI literacy. As prompting becomes a primary interface for interacting with AI, understanding how prompts shape outcomes becomes a critical skill. Prompt literacy, as described by the authors, involves not only learning how to obtain effective outputs but also recognizing the ethical, cultural, and epistemic implications embedded in each interaction.

Creativity expanded and constrained by language

Generative AI systems have expanded access to artistic production by allowing users to generate images, music, and text through natural language instructions. However, the authors argue that this expansion comes with constraints that are often overlooked.

Creative practice frequently relies on tacit knowledge, embodied skills, and material engagement. Prompting, by contrast, translates creative intent into linguistic descriptions. This translation can standardize expression around what is easily described in words, narrowing the range of possible outputs.

The authors suggest that while prompting opens new creative spaces, it also risks homogenizing artistic production. Models trained on existing cultural data may reproduce dominant styles and conventions, especially when guided by prompts that prioritize clarity and optimization. This dynamic raises questions about originality, diversity, and the future of creative labor.

At the same time, the editorial acknowledges that prompting can support experimentation and collaboration across disciplines. By enabling rapid iteration and exploration, prompting lowers barriers to creative engagement. The challenge lies in balancing accessibility with depth, and innovation with diversity.

Prompting as a socio-technical governance challenge

The way prompts are designed and standardized can influence policy analysis, risk assessment, and administrative efficiency. The authors argue that prompting practices must be integrated into governance frameworks alongside transparency, accountability, and ethical oversight. Without such integration, prompting risks becoming an invisible layer of influence that shapes outcomes without scrutiny.

Future research directions identified in the editorial include explainable prompting, which aims to make the effects of prompts more transparent, and multimodal prompting, which integrates non-linguistic inputs to reduce overreliance on text alone. The authors also call for cultural and linguistic diversity in prompting practices, noting that prompts are shaped by language, norms, and context.