Most teams ship AI workflows without knowing if the output is actually good. Evals fix that. This guide shows you what they are, why they compound, and how to build your first one.
Includes a worked example, annotated rubric, and a blank template you can adapt to any marketing workflow.
An eval is a structured way to measure whether AI output meets a defined quality bar. Not “does it feel right” — does it pass specific, documented criteria that you decided on before the output was generated.
In practice, an eval has three parts:
1. A rubric — the criteria you’re scoring against. What does “good” mean for this task? Be specific enough that two people would score the same output the same way.
2. A pass/fail threshold — the minimum score an output needs to ship without human editing. Everything below the threshold gets flagged for review.
3. A feedback loop — when outputs fail, you diagnose why and adjust the prompt, the data, or the rubric. This is what makes evals compound over time instead of staying flat.
Say your team uses AI to generate subject lines for a weekly product newsletter. Here’s what an eval looks like for that workflow.
| Criterion | What you’re checking | Pass |
|---|---|---|
| Length | Under 50 characters (doesn’t get clipped on mobile) | Y/N |
| Specificity | Names the actual topic, not a vague category | Y/N |
| Tone match | Matches brand voice (peer-level, not salesy) | Y/N |
| No clickbait | Doesn’t overpromise or use manipulation tactics | Y/N |
| Differentiation | Wouldn’t be confused with last week’s subject line | Y/N |
Pass threshold: 5/5 to ship without review. 4/5 gets a human edit. Below 4 gets regenerated.
Both of these were generated by the same AI prompt for a product newsletter about a new reporting dashboard feature. The rubric catches what vibes-based review misses.
The first one “feels” fine on a quick scan. Without the rubric, it probably ships. With the rubric, you catch the specificity and tone problems before your audience does.
Evals aren’t a one-time quality check. They’re a feedback loop. Each week, you review what failed, adjust the prompt or rubric, and the pass rate goes up. Here’s what that looks like in practice:
By week 4, the AI handles 89% of outputs without human editing. The remaining 11% get flagged for review — not because the system failed, but because you designed it to route ambiguous cases to a person. That’s not a bug. That’s the system working.
Every eval cycle makes the next one better. The rubric gets sharper. The prompts get more precise. The edge cases get documented. This is what separates teams that “use AI” from teams that get compounding value from it.
“This looks good” is not an eval. If you can’t explain why it’s good in terms someone else could apply consistently, you don’t have a quality bar — you have a gut feeling. Gut feelings don’t scale, and they don’t compound.
If you write the criteria after seeing the AI’s output, you’ll unconsciously write criteria the output already passes. Define what “good” looks like before you generate anything. The rubric should be the spec, not the rationalization.
Some AI workflows are better served by a human spot-check than a formal scoring system. If the output is low-stakes, low-volume, and the person reviewing it has strong judgment — a quick read-through might be the right eval. Not everything needs a spreadsheet.
PDF with the full rubric example, the flywheel framework, all three mistakes, and a blank rubric template you can adapt to any marketing workflow.
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