---
id: "t-2026-06-25-10-10-10-layered-checks-that-make-ai"
type: "thought"
title: "10% × 10% × 10%: layered checks that make AI errors rare"
date: "2026-06-25"
week: "2026-W26"
url: "https://nosov.ai/writing/10-10-10-layered-checks-that-make-ai/"
linkedin: "https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A7475833128536961024"
author: "Ivan Nosov"
---

# 10% × 10% × 10%: layered checks that make AI errors rare

10% x 10% x 10% = 0.1% error rate.

That is the promise behind stable AI work.

AI hallucinations are real. The useful question is how to make them rare enough, visible enough, and contained enough that real work can move.

One model may miss. A second layer should catch most of what survived. A third layer should catch most of what remains.

That multiplication only works when each layer checks a different object.

That became the architecture question while building the HR Digital Twin from my last post. Once the system started producing real artifacts - executive decks, job offers, comp dashboards, policy answers - the design standard changed.

Where do we stop hallucinations before they become expensive?

The answer I've built around has two roles: Judge and Fixer.

The Judge checks the generated story before artifact generation. It asks whether the logic is grounded, complete enough, and safe to turn into a deck, dashboard, offer, or recommendation.

If the Judge fails the story, the system fixes the story and sends it back one step. The artifact is not generated from a weak narrative.

The Fixer works at the end. It checks the produced artifact against the accepted story and repairs final defects: one wrong number, one stale claim, one missing caveat, one bad wording choice.

Judge checks the story.

Fixer checks the artifact.

I see that common knowledge treats AI as a generator: prompt in, answer out, human reviews the damage. That works for low-risk drafting. It breaks when the output becomes a business artifact.

In engineering language, this maps to evaluator plus post-generation repair. For a business function, the language is simpler: do not generate the artifact until the story passes; do not deliver the artifact until the facts are fixed.

The 10% x 10% x 10% logic is not a benchmark, but the smart design intuition.

Stack different checks at the points where errors change form.

Before reasoning becomes a polished artifact.

Before the polished artifact reaches the user.

The question for every AI-first organization is now practical:

where is the Judge, where is the Fixer, and what work is allowed to leave the system without them?

Note: Infographic generated with Gemini based on my story.