How Do You Know Your AI Will Work? A Practical Guide to Quality Assurance

After we complete their AI strategy, every company I work with asks the same question: “How do we know this will actually work?” They want proof, not promises. They need documentation, not declarations. Most importantly, they require a systematic way to measure quality at scale before they put AI in front of customers, employees, or shareholders.

The answer is evals. If you’re deploying AI systems in production, evals are the quality assurance framework that stands between pilot success and enterprise failure. Let’s review.

The Quality Assurance Gap

Some companies spend months creating their AI strategies. Then, they write detailed product requirements documents (PRDs) that specify exactly what the system should do. Then, they develop critic frameworks (rubrics) that define acceptable output criteria, manually spot-check, and deploy. This is not a best practices approach.

PRDs tell you what to build. Critic frameworks tell you how humans should judge quality. Neither one tests whether your AI system actually delivers consistent quality on request number 10,000. That requires evals.

What Evals Actually Are

An eval (evaluation) is a structured test suite that measures AI system performance against specific success criteria. You create a dataset of inputs, define expected output characteristics, establish scoring rules, and run automated tests that tell you whether the system meets your standards.

Suppose you’re deploying an AI assistant to help customer service representatives respond to product questions. Your eval might include 200 sample customer questions, scoring criteria for each response (accurate information, appropriate tone, proper escalation triggers, brand voice compliance), and pass/fail thresholds. You run these 200 tests every time you modify the system. If performance drops below your threshold, you know immediately.

How to Create Effective Evals

Building evals requires the same rigor you’d apply to any quality assurance system. Start by defining your task clearly. Vague tasks produce vague evals. “Generate marketing copy” is too broad. “Generate email subject lines under 60 characters for B2B SaaS product launches” is specific enough to test.

Next, create your test dataset. Collect 50 to 200 examples of real inputs your system will encounter. Include common cases, edge cases, and adversarial cases (inputs designed to expose failures). For each input, define expected output characteristics. You don’t need to write the perfect output, just the criteria any acceptable output must meet.

Scale your test set size to risk level. Start with 20 test cases for low-stakes applications like internal tools or marketing copy. Use 50 to 100 cases for medium-stakes deployments like customer service assistants. For high-stakes applications in healthcare, legal, financial services, or compliance-critical domains, build 200 or more test cases to adequately cover edge cases and failure modes.

Define your scoring system. Binary pass/fail works for compliance requirements (legal disclaimers present, no prohibited terms used). Scaled scoring (1 to 10) works for subjective qualities (brand voice alignment, persuasiveness). Weighted scoring works when some criteria matter more than others. For subjective scoring criteria, consider using multiple human evaluators on a sample of test cases and measuring inter-rater reliability. If evaluators consistently disagree on scores, your criteria need clearer definitions or examples.

Run your eval on a known-good baseline. If you’re replacing a human-powered process, test the eval against outputs you already consider high quality. Your eval should identify those outputs as passing. Then test against known failures. Your eval should catch them. This validation step catches scoring problems before they contaminate your production monitoring.

It’s important to keep eval test cases strictly separate from any examples used to train or tune your AI system. Contamination between training data and eval data can invalidate your quality measurements.

Maintaining Evals Over Time

Treat your eval as a living document, not a one-time deliverable. Evals degrade without maintenance. Requirements change. New failure modes emerge. AI capabilities improve. Evals need to be constantly tested and improved.

Add new test cases when you discover failures in production. Update scoring criteria when business requirements shift. Remove outdated examples that no longer reflect real usage. Track eval performance over time so you can see whether your system quality is improving, stable, or degrading.

Trigger immediate updates when you see consistent failures in any category, when client requirements change materially, or when you deploy significant system modifications.

Watch for optimization to the eval itself rather than real quality assurance. If your system scores perfectly on evals but fails in production, your eval has become the target rather than a measure. Add new test cases that reflect actual failure modes you discover in deployment.

Version your evals using semantic versioning (for example, v1.2 for minor updates, v2.0 for major changes). Track which eval version was used for each production deployment in your release notes. This creates clear audit trails for compliance reviews and lets you compare system performance across different eval versions when troubleshooting quality regressions.

Why This Matters for Enterprise AI

Evals provide four critical benefits for organizations deploying AI at scale:

Risk reduction. You catch quality failures before they reach customers. A single compliance violation in production can cost millions. Evals catch those violations in testing.

Cost control. Manual review doesn’t scale. If you’re generating 10,000 outputs per day, you need automation. Evals reduce human review cycles by 60 to 80 percent while often improving quality scores.

Compliance documentation. Regulators and auditors want proof your AI systems meet standards. Eval performance data provides that proof. You can show exactly what you tested, what passed, what failed, and what actions you took.

Continuous improvement. Evals give you an optimization feedback loop. Change a prompt, run the eval, measure the impact. You can improve systematically instead of guessing.

Remember evals only catch failures you anticipated when you designed them. They’re necessary but not sufficient for quality assurance. Even comprehensive evals cannot guarantee zero failures in production. Monitor production deployments closely, especially during the first 30 days, and treat any failures your evals missed as high-priority additions to your next eval version.

Building Your First Eval

If you’re planning an AI deployment, ask yourself three questions. First, do you have documented quality standards beyond “we’ll know it when we see it”? Second, can you test those standards automatically at scale? Third, do you have a systematic process to update those tests as requirements evolve?

If the answer to any question is no, you need evals. The good news is that building evals is straightforward. Here are a few examples to help you gain a deeper understanding. And, of course, if you’re interested in learning more, please contact us.

Author’s note: This is not a sponsored post. I am the author of this article and it expresses my own opinions. I am not, nor is my company, receiving compensation for it. This work was created with the assistance of various generative AI models.