mobilizing decentralized publishing & review to constrain AI output

The plain language, human readable, finite 'file' input Retrieval-Augmented Generation (RAG) promises a corrective in relation to the black box of LLM inclination towards 'slop' output represents the prospect for a 'ground truth' that engaging with the LLM's prompt interface can be measured against (in various ways & against various trained builds of the LLM's weights, etc [however I ought to reference the variable in the structure, ie, 'where the effectiveness{?} is found {?} in the models 'software data'])