GenAI testing in the spotlight: Methods for secure and reliable models

Why GenAI testing is relevant now

Generative AI (GenAI) is no longer a topic for the future: it is finding its way into numerous software products – from text generators and code completion to chatbots based on large language models (LLMs). The possibilities seem limitless. But with great power comes great responsibility: quality assurance for such systems presents QA teams with new challenges.

GenAI in software development

Unlike traditional programs, GenAI models are probabilistic systems. The same inputs do not necessarily produce the same outputs. This non-deterministic nature requires a rethink of established testing strategies. At the same time, GenAI offers great potential – for example, in the generation of test data or in automated bug reporting.

Risks and challenges

• Hallucinations: Models invent apparent facts.
• Bias: Discriminatory responses can damage your image.
• Data protection: Confidential content must not be reconstructable.
• Explainability: Decisions often remain opaque.

What is GenAI testing?

Differentiation from classic software testing

In classic testing, we check functional requirements against expected results. GenAI testing, on the other hand, evaluates the quality, reliability, and ethics of generative models.

Specific features of LLMs and generative models

• Black box character
  No deterministic behavior
  Constant updates to learning content
  Sensitivity to input variations (prompt engineering)

Methods for GenAI testing

Black box vs. white box testing

• Black box: Focus on input/output validation in production-like scenarios
  White box: Access to model details, suitable for research and audits, but very difficult due to high complexity

Prompt engineering and evaluation

Carefully formulated prompts enable reproducible tests. Important evaluation criteria: factual accuracy, stylistic consistency, lack of bias, and contextual understanding.

Test data generation and validation

GenAI can even be used for synthetic data, adversarial testing, and edge cases. Validation is performed using gold standard data sets or benchmarks.

Tools for GenAI testing

Tools in comparison

• OpenAI Eval: Evaluation of LLM outputs
  Promptfoo: Prompt testing with metric tracking
  DeepChecks for LLMs: Automated quality checking

Selection criteria for QA teams

• Openness and adaptability
  Automation
  Transparent metrics (e.g., BLEU, ROUGE, toxicity score)
  CI/CD integration

Integrate GenAI testing into QA processes

Automation

APIs and scripting tools enable automated A/B testing or prompt variant comparisons.

Continuous integration & pipelines

• Tests as part of the build pipeline (e.g., via GitHub Actions)
  Threshold values as gating criteria
  Regression tests for model updates

Conclusion and recommendations

GenAI testing is not a "nice to have" but essential for companies that want to use AI responsibly. It requires new testing strategies, tools, and skills.

Tips for QA teams:

• Get involved in model development at an early stage
  Build up prompting skills
  Continuously evaluate your toolset
  Work with benchmarks