AI Testing / Generative AI Testing
AI Testing is the process of validating, verifying, and monitoring AI systems to ensure they behave correctly, reliably, ethically, and safely under real-world conditions. Unlike traditional software testing, AI testing focuses on probabilistic outputs, data dependency, and model behavior rather than deterministic logic.
Generative AI (GenAI) Testing specifically evaluates models that generate content—such as text, images, code, audio, or video—to ensure outputs are accurate, relevant, unbiased, secure, and aligned with user intent and business goals.
Key Objectives of GenAI Testing
- Output Quality: Accuracy, relevance, coherence, and usefulness of generated content
- Safety & Ethics: Detection of harmful, biased, toxic, or hallucinated outputs
- Robustness: Stability across varied prompts, edge cases, and adversarial inputs
- Compliance: Adherence to legal, regulatory, and organizational policies
- Performance: Latency, scalability, and cost efficiency
- Prompt Reliability: Consistency and predictability of responses to similar inputs
Common Testing Techniques
- Prompt testing and prompt regression
- Bias and fairness evaluation
- Hallucination detection
- Red-teaming and adversarial testing
- Human-in-the-loop evaluation
- Automated evaluation using benchmarks and metrics
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