2026-05-08 · 10 min read
Quality-Checking AI With Multi-Model Review
A concrete eval pattern: precision, coverage, and genericness scores — when to regenerate, and how to keep customer-facing copy out of the jargon zone.
Why one model isn't enough
A single LLM can be confidently wrong. In operations software, that's payroll-grade risk. We run a primary model for generation and a review model that scores the output before it hits a user.
Dimensions we score
| Dimension | Question |
|---|---|
| Precision | Are facts grounded in the provided context (page text, structured fields)? |
| Coverage | Did we miss an obvious surface (e.g. careers page implies hiring — did we mention staffing ops)? |
| Specificity | Does this read like a tailored brief — or generic consulting filler? |
Aggregate the three into a single pass/fail threshold (we use ~0.7 average). One failed regeneration pass is usually enough; infinite loops are worse than a human handoff.
Customer-facing vs engineering vocabulary
The pipeline names can be technical (cross-modal eval). The product copy cannot. Marketing promises should sound like: "Reviewed by a second AI before your team sees it" — not "Opus vs GPT judge layer".
Operational note
Log scores and model IDs per run. When quality drifts, you can bisect whether it's the prompt, the model version, or the input scrape quality — not guess.