Why 'Don't Be Generic' Doesn't Work

Asked for a competitive analysis. Gave it everything. Market position, three-year data, the specific situation. "Be insightful. Don't be generic."

The output was structured, fluent, professional. And interchangeable with what it would have produced for any company in any market. Every recommendation could have been copy-pasted into a competitor's strategy doc without changing a word.

Twenty controlled runs confirmed what was visible. Specific versus vague, crossed with positive versus negative framing. Four combinations. Pure negation ("don't be generic," "avoid cliches," "don't use buzzwords") was indistinguishable from giving no instruction at all.

That removes a label without providing a destination. The model left the default and wandered to the adjacent region. Same neighborhood. Different house number. Negation names what to avoid. The default already routes around that. Only an anchor forces a different path.

Then the other cell. Same negation, but paired with specifics: "Don't include recommendations that could apply to any B2B SaaS company. Every recommendation must reference Northvane's specific assets, 5 years of shipping logistics data, 12 engineers, Pacific Northwest enterprise incumbents."

Strong effect. The direction replicated across generators. This is the content-specificity lever. Stacking more explicit constraints is a different lever, from a different experiment. Keep the two separate.

The specificity isn't measurably adding analytical quality. It's adding verifiability.

"Don't be generic" blocks one path. The model takes the next most likely path, which is a variation on generic. "Reference these specific assets" creates an anchor the output has to pass through. The result physically cannot be the same for a different company. The constraint tests itself.

In blind testing, a domain expert couldn't distinguish specific from generic outputs on quality. Picked specific 3 out of 5 times. Chance level. The specificity instruction changes what the output looks like: more data references, more grounded claims. It does not, measurably, change what the output says.

The demonstrated value is verifiability. The specific output can be checked. Every claim traces to something nameable. The generic output makes the same points but you can't verify them. When you need to trust the analysis, specificity makes the output auditable. When you're using it as a starting point for your own thinking, it doesn't matter.