Framework

Truth at the Foundation

Hallucination isn't a model problem. It's a data architecture problem. Fix the foundation, not the output.

A framework by Jason Alan Snyder, Co-Founder and Chief AI Officer of Artists & Robots.

What it means

When an AI system states something false, the usual response is to correct the answer or tune the model. Truth at the Foundation says the fault is upstream: a system that cannot say where a fact came from, how fresh it is, or how much to trust it will keep producing confident errors no matter how the output is polished.

The fix is architectural. Score data before it is used. Carry provenance with every claim. Make the system show its sources, so a false statement has nowhere to hide and a true one can be checked.

How the studio applies it

Every answer the homepage agent gives lists the sources it used, and every stored brief on this site keeps those links. A reader can open each one. That is the foundation doing the work, not a disclaimer on the output.

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