Reliability measures how consistently a data-analysis system delivers accurate and timely results under different conditions. It is not the same as simply being available: a system can be operational yet still produce inaccurate results. section 7.1
Evaluate reliability by checking three practical questions:
- Does the system return accurate results consistently? section 7.1
- Does it return those results in a useful timeframe? section 7.1
- How often do failures or inaccuracies occur, and how serious are their effects? section 7.1
Reliability depends on the setting
An astronomical observatory may tolerate slower responses, while a hospital patient-monitoring system cannot. A chemical-plant automation system may need many concurrent, linked processes to provide operators with a reliable picture. The acceptable balance therefore depends on the consequences of delay, failure, or inaccurate output. section 7.1
When generative AI performs analysis, reliability improves when it produces Python code that can be reviewed and run. Asking it to rely only on inherent language abilities is easier for beginners but more error-prone, less scalable, and harder to verify because the process remains a black box. section 5.3.2
Use practical checks before trusting an AI-assisted result:
- Inspect the generated code and review whether its logic matches the intended analysis. section 5.3.2
- Keep pasted text manageable and split longer inputs to reduce truncation and incomplete results. section 5.3.2
