Palantir-style knowledge reuse: a five-check test for AI assets

A familiar frustration: an AI-produced procedure or piece of code is saved, but the next person cannot use it because the premises are missing, and no one knows which documents to fix when a business rule changes. Storing outputs, it argues, is not the same as gaining the ability to reuse them. Its reference point is Palantir, which the author says describes a structure for returning field knowledge from forward-deployed engineers to shared product functions.

The proposed answer is to make the reusable unit smaller than a finished deliverable. In a fictional delivery-inquiry example, what transfers is not a saved reply but the definitions and judgments behind it — when to treat a shipment as delayed, what must not be concluded when records are missing, and who should receive the case. Such definitions need their target, official source, test examples, and consumers attached before another person or an AI can handle them reliably.

What keeps this from becoming a pile of documents is pairing registration with retirement. Definitions move through candidate, verified, adopted, suspended, and retired states, and changing an adopted definition means checking dependent processes, search targets, templates, and caches. The author stresses that the tables are a design proposal, not production operating procedures, and that no implementation or effect measurement has been carried out.