Working With the Machine
The most valuable prompting skill isn't writing better prompts. It's knowing when prompting isn't the answer.
The Real Skill: Knowing When to Stop
The most valuable prompting skill isn’t crafting better prompts. It’s recognizing when prompting isn’t the answer.
If you’re on your fifth iteration of a prompt, trying to get reliable behavior for a critical task, that’s a signal. The task might not be well-suited for current LLMs.
What Tends to Work
Format specificity: ‘Return a JSON object with these fields’ eliminates ambiguity about output structure.
Examples over descriptions: Showing what you want often works better than explaining it.
Task decomposition: Breaking complex tasks into steps often improves results.
Constraints Beat Personas
Giving the model a persona (‘You are an expert marketer’) is popular. It works sometimes, but has a failure mode: the model may confidently perform the persona even outside its actual competence.
Constraints often work better than personas. ‘You are an expert’ makes the model confident; constraints help it stay appropriately scoped.
The Takeaway: Prompting is interface design between human intent and machine capability. Be specific about format, use examples, decompose complexity, and know when a different approach is needed.