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Turn a deal thesis into a data-pull recipe

Rung 4 of 10 · Recipe pattern

1 · Learn the move · Recipe pattern

Everyone buys the same lists off the same data platforms and mails the same blind offer. The edge is the QUERY: the filter combination that isolates the motivated, least-competed owners before anyone else pulls them. The recipe pattern turns a plain-English thesis, say, out-of-state owners of five or more vacant parcels with recent tax delinquency, into the exact filter steps for your platform (PropStream, DataTree, a county GIS export), so a junior VA can pull an expert-grade list. Two things keep this clean and safe. First, the output is a set of FILTER CRITERIA, not a list of people and not any fact about an owner, so there is nothing for the AI to fabricate: it translates your thesis into clicks, it never invents a name, a balance, or a phone number. Second, every filter targets owner BEHAVIOR or STATUS, absentee, tenure, portfolio size, tax status, parcel type, never a protected characteristic like race, religion, national origin, or familial status; targeting or excluding on a protected class is a legal line, and where a thesis drifts near it the recipe flags it for counsel instead of encoding it.

Turn this thesis into the exact PropStream filters: out-of-state owners of vacant land, 5+ parcels, tax-delinquent.
[name your platform and any fields you know]

2 · Your turn. You write the prompt

You want the least-competed sellers in a county. Your thesis: owners who live out of state, hold five or more vacant parcels, and are behind on property taxes, because absentee portfolio owners with a tax problem are the most likely to take a cash offer. You use PropStream. You want the exact filter recipe your VA can follow to pull the list, without the AI inventing owners or drifting into targeting you can't defend. Write that prompt.

Remember: the AI sees only your prompt, not this page. If the situation isn't in your prompt, it doesn't exist.

Optional. These shape the output when you run your prompt below, not your score.