How to Test AI Applications and ML Software: Best Practices Guide

Testing Artificial Intelligence systems should be based on a fundamentally different approach than old-school software testing.

Traditional software follows clear rules and produces predictable outputs. AI solutions learn from data and make probabilistic decisions.

The consequences of inadequate AI testing result in biased hiring recommendations, inaccurate healthcare information, or misclassified objects in safety-critical situations.

What makes AI testing particularly challenging is its complexity. Traditional software either works correctly or fails obviously. AI systems can appear to function well while hiding subtle problems that only emerge in specific situations or with certain data inputs.

The EU AI Act introduces clear requirements and significant penalties for non-compliant systems. Organizations need to implement robust testing frameworks not just for technical performance, but also for fairness, transparency, and privacy.

The cost of not properly testing AI systems — in terms of regulatory penalties, reputational damage, and potential harm — far outweighs the investment in proper testing procedures.

This article is all about them.

Key takeaways

AI fails differently. Traditional software crashes. AI gives wrong answers that look right.

Data testing comes first. Bad data guarantees bad models. Quality checks prevent 30-50% of AI failures.

Three-layer testing approach. Test the foundation, the model itself, and real business impact.

Non-deterministic challenges. The same inputs can yield different outputs. Use statistical testing instead of exact matches.

Ethical testing isn’t optional. EU AI Act penalties are severe. Bias testing is now a legal requirement.

Specialized metrics matter. Use AI-specific metrics: AUC-ROC, precision/recall, RMSE, BLEU, perplexity.

Generative AI needs unique approaches. LLMs require specialized testing for hallucinations and prompt sensitivity.

Continuous monitoring is essential. Models degrade as real-world data shifts. Monitor constantly.

Documentation as defense. Document limitations and test results to protect against compliance issues.

Cost-benefit reality. Thorough testing costs more upfront but delivers 4-5x ROI through reduced failures.

Why Test AI Applications at All?

Unlike traditional software, AI and ML systems aren’t programmed explicitly — instead, they learn from data. This makes them powerful but introduces peculiar risks and uncertainties.

Accuracy and reliability. Even small errors in AI predictions can significantly affect business operations and user trust. Continuous testing of AI applications identifies inconsistencies and improves prediction reliability.

Risk of bias. AI models learn from data that often reflects existing biases. Testing helps your models to remain fair and compliant with ethical standards and regulations.

Security and privacy. AI-driven systems frequently handle sensitive data. Security testing reveals vulnerabilities and protects data integrity, confidentiality, and user privacy.

Regulatory compliance. Increasingly strict regulations around AI (e.g., EU AI Act, GDPR, HIPAA) require robust testing documentation. Failing compliance = heavy penalties and brand damage.

Robustness and stability. Users expect AI applications to perform consistently under real-world conditions. You need to make sure your model maintains stable performance despite unexpected inputs or scenarios.

If you don’t, you risk unreliable outputs, reinforce harmful biases, violate compliance standards, or expose sensitive information.

Current Challenges Associated with Testing AI Software

We will not talk much here about standard problems and tech issues every software has. You know those already. Let’s focus on challenges of testing machine learning models and Gen AI tools that are caused by their inherent complexity and learning-based nature.

Technical challenges

Non-deterministic outcomes. AI models can produce different results even with identical inputs. It complicates validation and verification. Unpredictability demands extensive testing and monitoring scenarios for consistent performance.

Complexity of training data and model behavior. Large datasets and sophisticated model architectures make finding the exact source of errors difficult. You need advanced testing solutions to analyze data quality, relevance, and coverage.

Versioning and reproducibility. AI models constantly evolve through retraining and updates. Managing model versions and reproducing past behaviors to validate improvements or identify regressions is technically demanding.

Adversarial vulnerability. AI products, especially deep learning ones, can be susceptible to adversarial attacks — inputs intentionally crafted to deceive models. Planned testing must consider methods that detect and defend against such vulnerabilities.

Resource intensity. AI and ML model testing often requires significant computational power and specialized infrastructure, making testing resource-intensive and potentially costly.

Operational challenges

In our experience, scale, complexity, and continuous evolution of machine learning workflows affect operational aspects of testing AI.

Integration into CI/CD pipelines. Traditional CI/CD processes often don’t effectively accomodate ML workflows. AI testing requires frequent model retraining, data updates, and performance validation, requiring specialized integrations.

Dataset management. AI model testing demands handling large, diverse datasets that must be continuously refreshed and validated. Efficient storage, access, and dataset versioning is critical but challenging to manage at scale.

Scalability and performance constraints. AI tests require vast computational resources and can quickly strain infrastructure.

Ethical and regulatory challenges in testing AI

Very soon, when speaking about how to test AI models, we will start not with performance or even security but with ethics and compliance of ML testing. The traditional software testing approach is no longer viable for planning QA for AI-based applications.

It’s fair. Regulators know that most of the companies have experienced QA teams to cover technical testing of AI systems and machine learning applications. But resilience of AI in terms of personal data vulnerability, bias risks and general applied ethics field requires both extra attention and extra regulations.

Bias detection and fairness

Bias isn’t theoretical — it has real-world implications. Consider Amazon’s recruitment AI, scrapped after it systematically disadvantaged female candidates due to historical hiring data biases. Bias audits and fairness testing methodologies, like IBM’s AI Fairness 360 toolkit, allow early detection and correction of biases.

Transparency and explainability

Healthcare AI recommending treatments without explaining the rationale already leaves doctors hesitant and confused, leading to slow adoption. Robust explainability testing, employing tools like SHAP, LIME, or Explainable Boosting Machines (EBM), ensures AI decisions are transparent, justified, and trustworthy.

Data privacy and protection

In 2021, an AI-driven banking app mistakenly exposed customer transaction details, resulting in a multi-million euro GDPR fine and damaged trust. Effective AI testing must enforce rigorous data anonymization practices and rely on secure testing environments.

Compliance with the EU AI Act

The EU AI Act introduces clear risk-based classifications (unacceptable, high, limited, minimal) with defined testing and documentation standards. Organizations should adopt comprehensive AI lifecycle documentation, maintain robust audit trails, and implement continuous compliance checks.

_Companies that neglect rigorous AI testing and transparent documentation face substantial financial penalties and possible product bans within EU markets. _

Dealing with ethical and regulatory challenges proactively mitigates risk and reinforces user trust, brand reliability. It also ensures your AI-driven solutions sustainably align with societal and regulatory expectations. “Testing for ethics” will be a new type of testing used for AI algorithms alongside compliance, security and usability testing.

Quick questionnaire for ethical AI testing
Use these simple questions to start evaluating your AI system’s ethical and regulatory readiness:

AI App Testing: Types, Tools, Differences

Testing AI applications requires a more comprehensive approach than traditional software testing. The unique characteristics of machine learning models — their probabilistic nature, reliance on data quality, and potential for unexpected behaviors — demand specialized testing methods. Here’s a breakdown of essential testing types for AI systems:

Data testing

AI performance directly depends on data quality. Poor or biased training data inevitably leads to flawed models, making data testing a critical first step.

Model validation testing

This testing validates that the model works as intended across various scenarios, not just on cherry-picked examples.

Security testing

AI systems introduce unique security concerns beyond traditional applications, including data poisoning, model stealing, and adversarial attacks.

Functional testing

Functional testing focuses on whether the AI system meets its specified requirements and performs its intended tasks correctly.

Load and performance testing

AI systems often have different performance characteristics than traditional software, with unique resource needs and potential bottlenecks.

*Bias and fairness testing *

Ethical considerations are crucial for AI systems to ensure they treat all users fairly and don’t perpetuate or amplify existing biases.

Generative AI-specific testing

Generative AI systems like chatbots and image generators require specialized testing approaches that evaluate the quality and appropriateness of outputs.

Automated Testing Frameworks for Generative AI

Unlike deterministic systems that produce consistent outputs for given inputs, generative AI creates novel content — text, images, code, audio — that can vary significantly even with identical prompts. This fundamental difference requires specialized approaches to testing generative AI applications.

Specific testing challenges of generative AI

Output variability. The same prompt can produce different outputs each time, making traditional exact-match assertions ineffective.

Hallucinations. Models can generate plausible but factually incorrect information that’s difficult to automatically detect without reference data.

Qualitative evaluation. Many aspects of generative output quality (creativity, coherence, relevance) are subjective and hard to quantify.

Prompt sensitivity. Minor changes in prompts can drastically alter outputs, requiring robust testing across prompt variations.

Regression detection. Model updates may fix certain issues while introducing others, making regression testing complex.

Key testing frameworks and tools

LangChain testing framework

Provides tools specifically designed for testing LLM applications.

from langchain.evaluation import StringEvaluator from langchain.smith import RunEvalConfig # Define evaluation criteria evaluation = StringEvaluator(criteria=”correctness”) # Configure test runs eval_config = RunEvalConfig( evaluators=[evaluation], custom_evaluators=[check_factual_accuracy] )

Strengths

• Visual interface for test management
• Supports multiple LLMs for comparison
• Enables version control of prompts

Limitations

• Limited support for non-text outputs
• Mainly focused on prompt engineering

TruLens

TruLens focuses on evaluation and monitoring of LLM applications.

from trulens.core import TruSession from trulens.evaluators import Relevance session = TruSession() relevance = Relevance() with session.record(app, evaluators=[relevance]) as recording: response = app.generate(“Explain quantum computing”) # Get evaluation results results = recording.evaluate()

Strengths

• Real-time monitoring capabilities
• Multiple built-in evaluators (relevance, groundedness, etc.)
• Works with major LLM frameworks

Limitations

• Steeper learning curve
• More focused on evaluation than comprehensive testing

MLflow with LLM Tracking

MLflow has expanded to support LLM testing.

import mlflow from mlflow.llm import log_predictions, evaluate_model # Log model predictions log_predictions( model_name=”my-llm”, inputs=test_prompts, outputs=model_responses ) # Evaluate model results = evaluate_model( model_name=”my-llm”, evaluators=[“factual_consistency”, “toxicity”] )

Strengths

• Integrates with existing ML workflows
• Comprehensive experiment tracking
• Supports model versioning

Limitations

• Requires additional setup for generative AI metrics
• Lacks specialized generative AI testing features

Deepchecks

Deepchecks provides data validation and model evaluation.

from deepchecks.nlp import Suite from deepchecks.nlp.checks import TextDuplicates, OutOfVocabulary suite = Suite( “Generative Text Validation”, checks=[ TextDuplicates(), OutOfVocabulary() ] ) results = suite.run(train_dataset, test_dataset, model)

Strengths

• Strong focus on data quality
• Detects drift and outliers
• Visual reporting

Limitations

• Less focused on creative aspects of generation
• Primarily designed for NLP models

Testing strategies for different generative AI outputs

Text Generation Testing

Assertion-based approaches

Content inclusion. Check that outputs contain key required information
Content exclusion. Verify outputs avoid prohibited content or misinformation
Semantic similarity. Use embeddings to assess closeness to reference answers

Example implementation

def test_response_contains_required_info(prompt, response): required_points = [“pricing options”, “delivery timeframe”] return all(point in response.lower() for point in required_points)

Image generation testing

Automated visual quality checks

• CLIP-based evaluation. Measure text-image alignment
• FID and IS scores. Assess perceptual quality and diversity
• Style and content consistency. Verify adherence to input specifications

Code Generation Testing

Functional validation

• Compilation testing. Verify generated code compiles without errors
• Unit test execution. Run generated code against test cases
• Static analysis. Check code quality metrics (complexity, maintainability)

Example approach

def test_generated_code(prompt, code_response): # Write code to temp file with open(‘temp_code.py’, ‘w’) as f: f.write(code_response) # Execute code with test inputs result = subprocess.run([‘python’, ‘temp_code.py’], input=’test input’, capture_output=True) # Check execution succeeded return result.returncode == 0

Automated testing workflow integration

To effectively integrate generative AI testing into development workflows.

1. Define test suites. Create collections of prompts and expected response characteristics.
2. Implement CI/CD pipelines. Automate testing on model updates or prompt changes

Example GitHub Actions workflow steps: – uses: actions/checkout@v3 – name: Run LLM tests run: python -m pytest tests/llm_tests.py – name: Evaluate model responses run: python evaluate_model_outputs.py

1. Set up monitoring. Track performance metrics in production to detect degradation

• Response quality scores
• User feedback metrics
• Factual accuracy rates

1. Establish feedback loops. Continuously improve test coverage based on production issues

Human-in-the-loop testing

Some aspects of generative AI require human evaluation:

Human evaluation processes

• Controlled A/B testing. Compare outputs of different models or prompts
• Quality rating scales. Define consistent criteria for human evaluators
• Diverse evaluator panels, Ensure different perspectives are represented

Automation opportunities

• Automated filtering. Use models to pre-filter outputs for human review
• Targeted evaluation. Direct human attention to high-risk or uncertain cases
• Learning from feedback. Use human evaluations to train automated classifiers

An NLP development team reduced manual review time by 65% by implementing an automated classifier that flagged only the 12% of outputs that fell below confidence thresholds for human review.

Test data management

Effective generative AI testing requires careful test data handling:

Representative prompt collections. Create diverse prompts covering various use cases, edge cases, and potential vulnerabilities

Golden dataset curation. Maintain reference outputs for critical prompts to detect regressions

Adversarial examples. Include prompts designed to challenge model limitations or trigger problematic behaviors

Version control. Track changes to test prompts and expected outputs alongside model versions

Measuring test coverage

Traditional code coverage metrics don’t apply well to generative AI. Instead, consider:

• Prompt space coverage. How well do test prompts cover the expected input space?
• Edge case coverage. Are boundary conditions and rare scenarios tested?
• Behavioral coverage. Do tests verify all expected model capabilities?
• Vulnerability coverage. Are known failure modes and risks tested?

The future of generative AI testing

As generative AI continues to evolve, testing frameworks are advancing to address emerging challenges:

• Multi-modal testing. Integrated testing across text, image, audio, and video outputs
• Self-testing models. Models that can evaluate and verify their own outputs
• Explainability tools. Frameworks that help understand why models generate specific outputs
• Standardized benchmarks. Industry-wide standards for generative AI quality and safety

By adopting these automated testing frameworks and strategies, development teams can deliver more reliable, accurate, and trustworthy generative AI applications that meet business requirements while managing the unique risks these systems present.

ML Software Testing Best Practices

Machine learning systems demand a fundamentally different testing mindset than traditional software. Where conventional applications follow deterministic rules, ML models operate on probabilistic patterns, creating unique quality assurance challenges.

Three layers of ML testing maturity

ML models are designed differently from anything we have seen before. That is why it requires unique testing approach — not just rigorous testing, but Quality Engineering that takes into account how the model is trained and which decisions based on that data will be made.

Think of ML testing as a pyramid with three distinct layers, each building upon the last to create increasingly robust systems.

Layer 1: Foundation testing

At the base of our pyramid sits the fundamental infrastructure that supports ML operations. This layer focuses on testing the technical components that enable model operations.

Testing at this level ensures your data pipelines, training processes, and deployment mechanisms function correctly.

• Data pipeline validation confirms data is flowing correctly from sources to training environments.
• Environment consistency checks ensure your development, testing, and production environments process data identically.
• Integration testing — API endpoints, data serialization/deserialization, and error handling — verifies that your model correctly interfaces with upstream and downstream systems.