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June 17, 2026 · All posts

Most teams shipping AI-generated text have evaluation debt

"This prompt worked last week. Why is quality bad now?" If that question keeps coming up, you're operating without a reliable way to measure quality.

Software teams learned decades ago that shortcuts accumulate. Ward Cunningham coined the term technical debt for the cost that builds up when you move fast without building the supporting infrastructure. AI teams are creating a similar problem: evaluation debt.

Evaluation debt happens when you ship AI-generated text without a reliable way to measure quality over time. It usually looks something like this:

• Quality seemed fine on Friday. Monday's outputs feel off.
• You change a prompt to fix one failure and create three new ones.
• A model upgrade unexpectedly changes behavior.
• A bad output reaches customers before anyone notices.

When this happens, most teams respond by rewriting prompts, swapping models, or tweaking parameters. But that's not quality control. It's trial and error.

The test layer you never built

The solution isn't more prompt iteration. It's a test layer. For traditional software, engineers don't deploy code changes without tests. AI-generated content needs the same discipline. That starts with two things:

1. A clear definition of what "good" looks like.
2. A test set that checks outputs against that definition.

Without those, every prompt change is blind. You can't tell whether a new prompt improved tone while quietly reducing accuracy. You can't detect regressions when you switch models. You can't measure whether quality is actually improving. So teams end up trapped in a cycle of constant prompt tweaking, never quite sure whether they're moving forward.

Paying down evaluation debt

The fix is straightforward: define the quality dimensions that matter: accuracy, tone, formatting, policy compliance, or whatever is critical for your use case. Build a representative test set, and evaluate changes before they reach production.

Once you do that, prompt updates become experiments instead of guesses. Quality becomes measurable. And shipping gets faster because you're no longer relying on intuition to determine whether a change worked. You can retest previous edits to see if they still hold up, so you're not starting from scratch every time.

If your team is constantly asking, "Why did quality suddenly get worse?" there's a good chance you're not dealing with a prompt problem. You're dealing with evaluation debt.