Technology · · 8 min read
Building Reliable AI Systems
Reliability in AI comes from engineering discipline around the model: evaluation, monitoring, fallbacks and clear ownership.
AI models are probabilistic. A reliable AI system acknowledges this and is designed so that occasional errors are caught, contained and corrected.
Practices that help
- Evaluation suites that reflect real usage
- Monitoring for drift in inputs and outputs
- Fallback behaviour when confidence is low
- Versioning of data, prompts and models
- Clear human ownership of outcomes
Reliability is less about the model and more about everything built around it.
Machine.sa editorial