Use Constraints Without Boxing the Model Into Nonsense

Constraints are useful when they rule out answers I cannot use. They become harmful when they are unrelated, conflicting, or impossible to check. If the output needs strict fields, Structured Outputs: When a Schema Beats More Prompt Words may solve the problem better than another paragraph of rules.

If I ask for a Python utility that must run without extra packages, “standard library only” is a must-have. “Use a cheerful tone” is a preference. If I need a game mechanic playable with paper cards tonight, “no networking or custom art” is a must-have. “Surprise me” is too vague to evaluate. Here is a constrained request: Propose three two-player puzzle mechanics. Each must work with ten paper cards and a timer, take under 15 minutes to teach, and require both players to make at least one decision. For each, give a 5-minute prototype test. If an idea requires a phone app, I can reject it. If it needs 30 minutes of rules, it fails the teachability limit.

“Be exhaustive in one sentence” is often impossible. “Do not ask questions, but make no assumptions” may be impossible when facts are missing. Resolve the tradeoff yourself or ask the model to flag a conflict instead of quietly ignoring one rule. I sometimes add: If any constraint makes the task impossible, identify the conflict and propose the smallest change before producing an answer. That avoids a polished but unusable response.

Negative instructions alone can leave the model guessing what you do want. Pair “do not use combat” with “focus on cooperation through exchanging information.” Pair “do not use external packages” with “use built-in Python tools.” Anthropic’s prompting best practices recommend clear, direct instructions and relevant context. Try it: Take a prompt with many rules. Mark each as must-have, preference, or irrelevant. Remove the irrelevant ones, resolve any conflicts, and write one check for each must-have. Keep learning: Structured Outputs: When a Schema Beats More Prompt Words, Compare Two Prompt Versions Without Fooling Yourself, and How to Prompt Across Models Without Pretending They Work the Same.