AI System Quality & Performance Testing
AI Quality Testing focuses on the operational excellence, reliability, and efficiency of machine learning models in production. Unlike security testing which focuses on malicious intent, quality testing ensures that the system meets its functional requirements, provides accurate and faithful information, and remains cost-effective. As LLMs are non-deterministic by nature, quality testing requires probabilistic evaluation frameworks (Evals) to maintain a high standard of user experience.
Reliability & Hallucination Testing
Identifying 'hallucinations' (factually incorrect but plausible outputs) and ensuring consistency across semantically similar queries.
Key Metrics
- {'Hallucination Rate': 'Frequency of ungrounded statements.'}
- {'Consistency Score': 'Variance in answers to the same intent.'}
- {'Faithfulness': 'For RAG, how well the answer aligns with retrieved docs.'}
- {'Answer Relevance': "Relevance of response to the user's actual prompt."}
Inference Performance Testing
Benchmarking the speed and responsiveness of the model under various load conditions.
Key Metrics
- {'Time to First Token (TTFT)': 'Critical for user-perceived speed.'}
- {'Inter-Token Latency': 'Smoothness of the streaming experience.'}
- {'Tokens Per Second (TPS)': 'Total model throughput.'}
- {'P99 Latency': 'Stability of response times for the 99th percentile.'}
Scalability & Concurrency Testing
Verifying the system's ability to handle spike traffic and scaling the underlying GPU/CPU infrastructure.
Key Metrics
- {'Concurrent Request Capacity': 'Max requests before TPS drops.'}
- {'Auto-scaling Latency': 'Time to spin up new inference nodes.'}
- {'Error Rate at Load': '5xx errors during high concurrency.'}
Model Drift & Degradation Monitoring
Detecting when a model's performance decays due to shifts in real-world data distribution (Data Drift or Concept Drift).
Key Metrics
- {'Population Stability Index (PSI)': 'Measuring distribution shifts.'}
- {'Prediction Drift': 'Monitoring changes in model output patterns.'}
- {'Feature Attribution Shift': 'Changes in which features drive decisions.'}
FinOps & Efficiency Testing
Optimizing the financial footprint of AI inference without sacrificing output quality.
Hands-on Lab Environment
Ready for the practical lab?
Apply the concepts learned in the AI System Quality & Performance Testing course within our virtual terminal environment.
Start Lab Terminal