Introduction: Not Prompt Engineering, but Knowledge Engineering

Interactions with large language models frequently begin with a measure of disappointment. A user poses a complex and potentially productive question, only to receive a response that is remarkably bland, hollow, or beside the point. This disparity is particularly acute in the domain of serious knowledge production: researchers who seek to employ artificial intelligence to analyse imperialist policies, document popular struggles, and disseminate counter-hegemonic narratives find that its outputs consistently fall short of expectations. Many conclude from this experience that artificial intelligence is unsuitable for rigorous intellectual work.

This conclusion is mistaken. The problem lies not in the tool, but in how it is used.

‘Prompt engineering’ has become the dominant framework for addressing this challenge, focusing on how to formulate language in order to elicit better responses from large language models. Consider the widely adopted CO-STAR framework — Context, Objective, Style, Tone, Audience, Response — in which four of six elements address matters of ‘form of expression’: how to say it, in what tone, to whom, and in what format. This is not without utility — form of expression matters, and we shall address it in due course. However, from the standpoint of serious knowledge production, this framework exhibits systematic blind spots: epistemological framework, information reserve, methodology, opinion and insight — dimensions that are decisive for the quality of any knowledge product — remain entirely absent from its purview.

To reduce the challenge of employing artificial intelligence to a set of techniques for ‘how to phrase things’ constitutes a fundamental misdiagnosis. A more accurate assessment is this: the root cause of most failed interactions with artificial intelligence lies not in the wording of the prompt, but in the user’s failure to systematically organise their own questions, contexts, positions, and purposes. In other words, this is not a problem of prompt engineering; it is a problem of knowledge engineering.

This article poses six critical questions that constitute a framework for the practice of knowledge engineering: What problem are we actually trying to solve? From what standpoint do we view the world? What does the AI need to know? How should the analysis proceed? What should the final product look like? And what is the researcher’s distinctive contribution? These six questions apply to any mainstream large language model, independent of any specific tool — they concern not technology, but how researchers themselves think about and organise the production of knowledge.

One: What Problem Are We Actually Trying to Solve?

Most disappointing interactions with artificial intelligence share a common characteristic: the user pro- ceeds directly to specifics without establishing a strategic purpose. ‘Help me write an article about the US-China tech war’ — such a request is virtually guaranteed to produce a superficial survey, because the request itself is superficial. Artificial intelligence is not incapable of producing work of analytical depth; rather, it cannot determine in which direction the desired ‘depth’ should extend.

Problem orientation is not ‘providing context’; it is an exercise in intellectual leadership. It requires the researcher, before engaging in any dialogue with artificial intelligence, to answer a fundamental question: What am I actually trying to understand? This question appears simple, yet it is precisely the step that most users omit.

Consider a concrete research project. In a study of the US-China semiconductor technology war, the researchers did not vaguely request an ‘analysis of US-China tech competition’, but instead formulated five progressively deepening core questions: How is the United States employing technology denial regimes to maintain geopolitical hegemony, and with what effects? How is China responding to technological containment, and what does this reveal about its development model? What is the current state of China’s supply chain dominance in critical sectors, and what are its implications? How is the technology war reshaping global supply chains and trade relationships? What are the implications for the Global South?

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