A stumbling block of the intermediate practitioner is trying to control too much
As someone uses AI more—and more systematically—something puzzling often happens: their results get worse. Prompts grow longer, more detailed, more specific. Edge cases are preemptively addressed. Explicit constraints are enumerated. And yet, the insights they receive in return become thinner, more conventional, and more brittle.
The conventional wisdom holds that clarity improves outcomes. Be specific about what you want. Don’t leave the system to guess. This is sound advice as far as it goes—vague prompts reliably produce hallucination, confabulation dressed up as analysis. But there is an opposite error, less obvious and more insidious, that emerges precisely when analysts become sophisticated enough to avoid the first trap.
Overspecification is the error of the experienced practitioner. It manifests as excessive constraint, not excessive clarity.
Consider how you walk. You don’t consciously direct each muscle, coordinate each step, manage balance moment to moment. You point yourself in a direction and let the built-in mechanisms work. The conscious mind sets intent; the motor system handles execution. When you think too carefully about the process—when you try to control each muscle—walking becomes awkward, unnatural, and, ineffective. So it is with AI. Your job is to establish the terrain and direction, not to control every footstep.
Context, Goal, and Process
Most overspecification stems from conflating three types of information that serve different purposes. Context describes the situation—business realities, constraints, strategic landscape, available resources. The goal is the core instruction: what you’re trying to accomplish—a solution, a strategy, a piece of code, an analysis. Process specifications dictate how to get there—frameworks to apply, analytical steps to take, output formats to generate.
Context enables reasoning. The goal directs it. Process specifications constrain it.
Consider an email marketing campaign. You’re launching for a B2B SaaS company with 5,000 existing customers, 40% enterprise and 60% mid-market, competing against three established players in project management software. The company positions itself on ease-of-use rather than feature depth. That’s context.
“Develop an email campaign strategy to increase customer engagement.” That’s the goal.
“Use a three-touch sequence.” “Include social proof in the second email.” “Emphasize ROI in subject lines.” Those are process specifications that preempt the analysis the AI should be doing based on the context and goal you provided.
The Mechanisms of Corruption
Overspecification damages analysis through several distinct mechanisms, each representing a different failure mode:
Exclusion of meaningful patterns. When process constraints are too specific, relevant precedents, analogies, and domain knowledge cannot enter consideration. The instruction set acts as a filter that blocks precisely the material that might produce insight.
Premature rejection of productive lines of thought. Reasoning paths that don’t immediately fit the specified framework are abandoned before they can develop. The AI, working to satisfy your constraints, cuts off exploration that might lead somewhere unexpected and valuable.
Misallocation of cognitive attention. In a fixed context window, tokens spent satisfying process constraints are tokens not spent on actual reasoning. Overspecification isn’t just adding problematic material—it’s displacing capacity for genuine analysis. The system expends its resources on meeting your specifications rather than solving your problem.
Prohibition of lateral insight. Cross-domain connections, unexpected analogies, creative approaches—these emerge from reasoning that moves freely within appropriate bounds. Tight process specifications prevent the very moves that produce non-obvious insight.
This analytical corruption happens because analysts try to prevent errors through specification. Each past failure gets memorialized as an additional constraint. This feels like progress—addressing known problems, building systematic practice. But overdone, the band-aid becomes a tourniquet. Unable to reason productively within excessive constraints, the system shifts from inference to pattern-matching, producing outputs that are trite, conventional, or strictly formulaic.
Develop a Theory of Mind
Piaget observed that young children struggle with what he called “theory of mind”—the recognition that other people have different perspectives, knowledge, and mental states than their own. A child shown a box of crayons that actually contains candy will, when asked what another person would think is in the box, answer “candy”—because that’s what they know. The child cannot yet model another mind’s different state of knowledge.
Adults develop this capacity naturally for human interaction. We adjust how we speak based on who we’re addressing. When you talk to a child, you simplify language. When you talk to your boss, you modulate tone. You’re modeling their likely interpretation and response, then shaping your communication accordingly.
Skilled AI collaboration requires developing the same capability. You need to understand how what you say will land, how the system is likely to respond. This means understanding what triggers template responses, which abstractions the system handles naturally versus which require explicit guidance, and when your phrasing shapes a conceptual space in ways that help or hinder analysis.
It means learning to distinguish context, goal, and process specification in your own thinking. The goal is to provide rich environmental information, state a clear intent, then keep process directives minimal and directional. The skill is not in writing longer prompts. It’s in writing prompts that establish clear goals within rich context, then trusting the system’s inference capabilities to work within those bounds. You point the AI in a direction. And then you let the built-in mechanisms walk.
Mastery in Practice
Meta-process management—the overhead of managing the collaboration itself—is a significant portion of analytical work with current AI systems. Overspecification increases this overhead rather than reducing it. Each constraint requires monitoring, each process instruction demands satisfaction, each specification creates brittleness that needs management when reality doesn’t conform to template.
The alternative is lighter, not heavier. But strategic–some constraint are necessary and non-negotiable. Establish the terrain clearly. State your goal explicitly. Provide the facts that matter. Then step back and let the analysis develop. When it goes wrong—and it will—resist the impulse to add more process constraints. Ask instead: did I provide sufficient context? Did I state my goal clearly? Am I fighting the system’s natural inference patterns rather than working with them?
The art of prompt engineering is learning to take the middle path: enough context, but not too much; enough specification, but not too much. Some constraints are necessary to focus analysis. Others become a weight around the AI’s neck. A technical constraint—”output as JSON”—serves a purpose. A process constraint—”use Porter’s Five Forces framework”—may focus or may foreclose. The real craft comes from learning how to curate instruction, and that comes only from extended practice and mindful dialogue.
The limitations of current systems are real. They can be frustrating. But they can also be worked with skillfully, even gracefully, once you understand their nature. The path to that skill runs through restraint, not elaboration. Through understanding what the system does well and letting it do that, rather than trying to control every aspect of its operation.
