Description
Generative AI Testing Overview
- Scope: Validate model outputs for correctness, relevance, safety, and alignment with product goals. 2. Data quality checks: Ensure training and evaluation datasets are representative, de-biased, and properly labeled. 3. Functional tests: Verify prompt handling, response formatting, token limits, and multi-turn coherence. 4. Robustness testing: Stress with adversarial prompts, edge cases, and noisy or malformed inputs. 5. Performance metrics: Measure accuracy, BLEU/ROUGE where applicable, perplexity, and task-specific scores. 6. Human evaluation: Design annotation guidelines, inter-annotator agreement checks, and blind A/B studies. 7. Safety and content policy tests: Detect hallucinations, toxic language, privacy leaks, and policy violations. 8. Explainability checks: Validate model rationales, attribution methods, and traceability of outputs. 9. Bias and fairness audits: Run subgroup analyses, counterfactual tests, and mitigation effectiveness checks. 10. Latency and scalability: Benchmark inference time, throughput, and behavior under load and degraded resources. 11. Regression testing: Maintain test suites and golden prompts to catch performance drift after updates. 12. Continuous monitoring: Instrument production for feedback loops, error rates, and user-reported issues. 13. Security testing: Probe for prompt injection, model extraction, and data exfiltration vulnerabilities. 14. Evaluation pipelines: Automate dataset versioning, reproducible experiments, and CI/CD integration. 15. Domain adaptation tests: Validate fine-tuned models on domain-specific benchmarks and real-world tasks. 16. Cost and resource analysis: Assess trade-offs between model size, accuracy, latency, and operational cost. 17. Governance and compliance: Ensure audit trails, documentation, and alignment with legal and ethical requirements.
