AI doesn't give generic answers because it is lazy or unintelligent. It gives generic answers because your instructions are generic. AI optimizes for the most likely response to your input — and the most likely response to a vague instruction is a vague answer. The fix is structural, not creative.
Diagnose your instructions free — 10 runs includedWithout a format or length instruction, AI defaults to the most common response type for that kind of request — which is usually a multi-paragraph essay. Specify the format explicitly: bullet points, numbered list, one sentence, a table.
AI calibrates vocabulary to a general audience unless told otherwise. If you need domain-specific language, name the domain and the audience: "Write for a senior SaaS product manager who knows Agile and OKR terminology."
Adding examples is a signal that the underlying instruction lacks specificity. Examples help, but they don't replace a clear structural specification. Diagnose which of the four gaps is the real problem and close it directly.
AI produces the most probable response to your input. A vague question produces a generic answer. The fix is to close the four specificity gaps — format, audience, role, and scope — in the instruction itself.
Specific enough that the instruction could only produce one type of output. If two very different responses could both satisfy your instruction, it is too vague.
Vague: "Write me a summary of this article." Clear: "Write a 3-bullet executive summary for a non-technical audience. Each bullet is one sentence. Cover only business implications." The clear version specifies format, length, audience, and scope. The vague one specifies none of them.
Workflow Doctor runs a specificity check across five quality dimensions, flags which dimensions are missing, and returns a repaired instruction with explicit format, audience, role, and scope constraints added.