Tech debt accumulates gradually. Eval debt explodes suddenly.
I want to tell you about a company I worked with last year. I won’t name them, but you’d recognize them.
They shipped AI features fast. Really fast. Eighteen months, dozens of LLM-powered features across their product. Leadership was thrilled. The press covered their AI transformation.
Then OpenAI pushed a model update.
Within 48 hours, their support queue exploded. Features that had worked for months suddenly behaved differently. Outputs that were reliable became unpredictable. Three customer-facing workflows started producing obviously wrong results.
The team scrambled to figure out what broke. Here’s what they discovered: they had no idea. Forty-seven AI features, zero systematic evaluation. No baselines. No regression tests. No way to know which features were affected without manually testing each one.
It took three weeks to fully assess the damage. Two features had to be rolled back entirely. One senior PM was quietly let go. The “AI-first” strategy became the “AI-cautious” strategy.
This is eval debt. And it’s coming for teams that don’t take it seriously.
Tech Debt vs Eval Debt
Tech debt is familiar. You cut corners to ship faster. The shortcuts accumulate. Eventually, you pay down the debt through refactoring.
The key characteristic of tech debt: it degrades gradually. Each shortcut makes the codebase a little worse. You feel the pain incrementally. You can prioritize cleanup when the pain becomes acute.
Eval debt is different.
flowchart LR
subgraph "Tech Debt Pattern"
TC1[Cut Corner] --> TC2[Ship Feature]
TC2 --> TC3[Slight Slowdown]
TC3 --> TC4[More Shortcuts]
TC4 --> TC5[Gradual Degradation]
TC5 --> TC6[Refactoring Sprint]
end
subgraph "Eval Debt Pattern"
EC1[Skip Evals] --> EC2[Ship Feature]
EC2 --> EC3[Works Fine]
EC3 --> EC4[More Features]
EC4 --> EC5[Still Fine]
EC5 --> EC6[Model Update / Drift]
EC6 --> EC7[Everything Breaks At Once]
end
style TC5 fill:#fef3c7
style EC7 fill:#fee2e2
Eval debt is invisible until it isn’t. Your AI features work. You ship more. They work too. You have no signal that anything is wrong - because you’re not measuring.
Then something changes. A model update. Data drift. A prompt that worked great in testing but fails on edge cases in production. Suddenly, you need to understand the behavior of every AI feature you’ve shipped.
And you can’t. Because you never built the instrumentation to understand them.
The Accumulation Pattern
Here’s how eval debt accumulates in practice:
Month 1: Ship first AI feature. Manual testing. “It looks good.” No formal evaluation.
Month 3: Ship three more features. Team is moving fast. “We’ll add evals later when we have time.”
Month 6: A dozen features in production. Someone proposes an eval framework. Gets deprioritized for new features.
Month 9: Twenty-five features. Different teams own different features. No consistent approach to testing.
Month 12: Model provider announces new version. Team wants to upgrade for better performance and lower cost.
Month 12, Day 2: “How do we know if the new model breaks anything?”
Month 12, Day 3: Silence.
The gap between features shipped and eval coverage is your eval debt. The orange line - hidden risk - is what happens when that debt gets called.
What Proper Evaluation Looks Like
Let me be concrete about what teams should be doing.
Baseline Measurements
Before any AI feature ships, you need baseline measurements:
- What does “good” look like for this feature?
- What’s the acceptable error rate?
- What are the critical failure modes?
- How will you detect regression?
These don’t have to be perfect. They have to exist.
flowchart TD
subgraph "Before Launch"
B1[Define Success Criteria]
B2[Create Test Dataset]
B3[Establish Baseline Metrics]
B4[Document Failure Modes]
end
subgraph "At Launch"
L1[Run Full Eval Suite]
L2[Compare to Baseline]
L3[Gate on Pass/Fail]
end
subgraph "After Launch"
A1[Continuous Monitoring]
A2[Regression Detection]
A3[Drift Alerting]
end
B1 --> B2 --> B3 --> B4
B4 --> L1 --> L2 --> L3
L3 --> A1 --> A2 --> A3
style B1 fill:#dbeafe
style B2 fill:#dbeafe
style B3 fill:#dbeafe
style B4 fill:#dbeafe
Regression Testing
Every model change, every prompt change, every context modification should trigger regression tests.
This is the eval equivalent of unit tests. You wouldn’t ship code without running the test suite. You shouldn’t ship AI changes without running evals.
Continuous Monitoring
Production behavior drifts. User inputs change. Edge cases emerge. Your evals need to run continuously, not just at deployment time.
A feature that worked last month might be failing now. Without continuous evaluation, you won’t know until users complain.
The Organizational Failure
Here’s the uncomfortable truth: eval debt is usually an organizational problem, not a technical one.
The tools exist. The techniques are known. The reason teams don’t build evaluation infrastructure is that it’s not prioritized.
Product pressure: “We need to ship features, not tests.”
Ambiguity: “How do you even test AI? It’s not deterministic.”
Short-termism: “It works now. We’ll deal with problems when they arise.”
Ownership gaps: “Is this the ML team’s job or the product team’s job?”
These are leadership failures, not engineering failures. The teams that survive the next three years will be the ones where leadership mandates evaluation as a first-class requirement, not an afterthought.
The Eval Maturity Model
We’ve developed a simple maturity model for AI evaluation:
| Level | Description | Characteristics |
|---|---|---|
| 0 - None | No systematic evaluation | "It looks right" testing. No baselines. Manual spot checks. |
| 1 - Ad Hoc | Evaluation exists but inconsistent | Some features have test sets. No standardization. Run manually. |
| 2 - Standardized | Consistent framework | Standard eval approach. Baselines documented. Runs before release. |
| 3 - Automated | CI/CD integrated | Evals run automatically. Deployments gated on pass. Regression blocking. |
| 4 - Continuous | Production monitoring | Live evaluation. Drift detection. Automatic alerting. Feedback loops. |
| 5 - Predictive | Proactive quality management | Predict regressions before they happen. Auto-remediation. Continuous improvement. |
Most teams are at Level 0 or 1. The survivors will be at Level 3 or above.
The Practical Path
If you’re starting from zero, here’s how to build evaluation infrastructure without stopping feature development:
Week 1-2: Inventory
Document every AI feature in production. Who owns it? What does it do? How would you know if it broke?
This alone is valuable. Many teams don’t have a complete inventory.
Week 3-4: Critical Path
Identify the three to five features where failure would be most damaging. These are your first evaluation targets.
Week 5-8: Baseline
For each critical feature, build a test dataset and establish baseline metrics. Document what “good” looks like.
Week 9-12: Automation
Integrate evaluation into your deployment pipeline. New releases don’t go out without passing evals.
Ongoing: Expansion
Systematically extend evaluation coverage to remaining features. Set a target: every AI feature must have automated evaluation within N months.
gantt
title Eval Infrastructure Buildout
dateFormat YYYY-MM-DD
section Foundation
Inventory & Assessment :a1, 2026-02-01, 2w
Critical Path ID :a2, after a1, 1w
section Baseline
Test Dataset Creation :b1, after a2, 2w
Baseline Metrics :b2, after b1, 2w
section Automation
CI/CD Integration :c1, after b2, 3w
Deployment Gating :c2, after c1, 1w
section Expansion
Remaining Features :d1, after c2, 12w
Continuous Monitoring :d2, after c1, 16w
The Career Risk
Here’s why I titled this “Eval Debt Will End Careers.”
When AI features fail at scale, someone is accountable. The PM who shipped without evaluation. The engineering lead who didn’t prioritize infrastructure. The CTO who didn’t mandate standards.
“We didn’t have time for evals” is not a defense. It’s an admission of negligence.
The teams that built evaluation infrastructure will weather model updates, data drift, and production incidents. They’ll catch problems before users do. They’ll be able to demonstrate due diligence.
The teams that didn’t will scramble, firefight, and eventually explain to leadership why they shipped AI features with no way to verify they worked.
I know which team I’d rather be on.
The Rotascale Approach
We built Eval because we saw this pattern repeating. Teams know they should evaluate. They don’t have time to build the infrastructure.
Eval provides serverless evaluation at scale - create test datasets, define metrics, run evals automatically in CI/CD, monitor continuously in production. The infrastructure is handled, so you can focus on defining what “good” looks like.
The goal isn’t to sell you a product. It’s to make sure evaluation happens. Build it yourself, use us, use a competitor - just don’t skip it.
Because when the model update comes - and it will - you need to know what broke.
Eval debt is invisible until it isn’t. Build evaluation infrastructure now, before you need it desperately.
Ready to pay down your eval debt? Eval provides serverless LLM evaluation with CI/CD integration. See how it works.