Evals: how to make AI systems reliable in production

AI evals (evaluations) are systematic tests that measure whether your AI system performs correctly before and after deployment. Three types: unit evals (specific input/output tests), regression evals (ensuring updates don't break existing behavior), and drift evals (monitoring accuracy over time). Without evals, you are flying blind — and 60% of AI systems degrade significantly within 6 months.

Why evals matter more than the model

Everyone obsesses over which AI model to use. Almost nobody talks about how to verify that the model is actually doing what you need it to do. This is the evaluation gap, and it is the number one cause of AI systems that work great in demo but fail in production.

An eval is a test — a systematic way to measure whether your AI system produces correct, consistent, and useful outputs. Without evals, you have no way to know if your system is working, degrading, or producing hallucinations that your team is blindly trusting.

The three types of evals every business AI needs

1. Unit evals

Like unit tests in software, these check specific inputs against expected outputs. "When a customer asks about return policy, the agent should cite the correct policy and provide the right timeframe." You build a library of these test cases and run them before every system update.

2. Regression evals

When you update prompts, change models, or add new capabilities, regression evals ensure you have not broken existing functionality. The most common failure mode in AI systems: you improve one thing and unknowingly break three others.

3. Drift evals

AI systems degrade over time as data changes, customer behavior evolves, and the world moves on. Drift evals run continuously in production, monitoring accuracy metrics and alerting when performance drops below thresholds.

How to build evals for a business AI system

1. Define success criteria: What does "correct" mean for your system? For a customer service agent, it might be: correct policy citation, appropriate tone, accurate order information. Write these down.
2. Build test cases: Create 50-100 representative scenarios covering common cases, edge cases, and known failure modes. Include the expected correct response for each.
3. Automate the testing: Run these tests automatically before every deployment and on a weekly schedule in production.
4. Set thresholds: Define minimum acceptable performance levels. If accuracy drops below 90% on a category, trigger an alert.
5. Review and expand: Every time the system fails in production, add that case to your eval suite. The suite grows smarter over time.

The business impact of evals

Companies that implement systematic evals see 50-70% fewer production incidents, 30% faster iteration cycles (because they can safely deploy updates), and significantly higher trust from internal teams who know the system is monitored.

Evals are not optional overhead — they are what separates a reliable AI system from an expensive experiment.

If you want help building evaluation frameworks for your AI systems, contact us.