AI Testing: How to Test AI Applications and Models — From Eval Metrics to CI/CD

Introduction

The world of artificial intelligence is changing rapidly. Just yesterday, we were amazed that a neural network could write text or recognize an object in a photo. Today, AI solutions are being integrated into business processes, replacing entire departments. But with opportunities come challenges: how to ensure the model works correctly? How to avoid "hallucinations" and data drift? The answer is simple — systematic AI testing.

Testing AI applications is not classical QA. There are no clear "expected results" for every input. AI models are probabilistic: the same prompt can yield different answers. Therefore, testing models requires a new approach: eval metrics, A/B tests, regression testing of prompts, and CI/CD for ML. In this article, we'll explore how to implement these practices in your AI product.

How to Test AI Models: Eval Metrics and Benchmarks

The first step is to define what "good" means for your task. For this, eval metrics and benchmarks are used.

Key Eval Metrics:

• Accuracy — the proportion of correct answers. Suitable for classification.
• Precision and Recall — metrics for imbalanced data (e.g., finding rare anomalies).
• F1-score — the harmonic mean of precision and recall.
• BLEU, ROUGE, METEOR — for text generation (machine translation, summarization).
• Perplexity — for language models: the lower, the more confident the model.

Benchmarks: Ready-Made Tests

Use public benchmarks to compare your model against a standard. For NLP — GLUE, SuperGLUE, SQuAD. For computer vision — ImageNet, COCO. For AI agents — AgentBench. But remember: benchmarks do not always reflect real model behavior in production. Always supplement them with custom tests.

A/B Tests: Testing Hypotheses on Real Users

Eval metrics on static data are only half the story. To understand how the model impacts business metrics, conduct A/B tests.

How to Organize an A/B Test for AI:

1. Split traffic — randomly direct users to the control group (old model) and the experimental group (new model).
2. Define success metrics — conversion, task completion time, CTR, retention.
3. Run the test for at least 1-2 weeks — to collect statistically significant data.
4. Analyze not only averages but also tails — the model may work well for 90% of users but fail on rare cases.

Example: AI support chat. The old model automatically answers 60% of requests. The new one — 75%. But the A/B test showed that the new answers are longer, and users are less likely to read them to the end. Conclusion: the "automatic ticket closure" metric increased, but NPS dropped. The model had to be refined.

Regression Testing of Prompts: Protection Against Regressions

Prompts are code. Any change to a prompt or the underlying model can break behavior. Regression testing of prompts is your shield.

How to Set It Up:

1. Create a test set of cases — 50-200 examples with expected characteristics (not exact answers). For example: "The answer should be polite, include a greeting, and provide a solution to the problem."
2. Automate the check — write unit tests that verify the presence/absence of key phrases, tone, response length.
3. Run tests with every change — both to the prompt and the model (when updating the LLM version).
4. Use evolutionary tests — add new cases from user complaints or rare scenarios.

Example: Prompt for a bank's AI assistant. After updating the model from GPT-4o to GPT-4.1, the test showed that the assistant stopped addressing the user by name. The regression was caught in 5 minutes, and the prompt was corrected.

CI/CD for ML: Continuous Testing of AI

Classical CI/CD (continuous integration and delivery) for AI is CI/CD for ML (MLOps). Without it, you risk deploying a "broken" model to production.

Main Pipeline Stages:

1. Data validation — checking the quality of new data (feature drift, missing values, outliers).
2. Model validation —