Fallback and error handling in Prompt Engineering / GenAI - Model Metrics & Evaluation

Metrics & Evaluation - Fallback and error handling

Which metric matters for Fallback and error handling and WHY

When a model faces unexpected inputs or errors, the key metric is robustness. This means the model should handle errors gracefully without crashing or giving wrong results. Metrics like error rate during fallback and successful fallback rate matter most. They show how often the system recovers correctly when the main model fails.

Confusion matrix or equivalent visualization

Fallback/Error Handling Outcomes:

|-----------------------------|
| Outcome           | Count   |
|-----------------------------|
| Correct prediction | 850    |
| Fallback success   | 120    |
| Fallback failure   | 20     |
| System error/crash | 10     |
|-----------------------------|
| Total             | 1000   |

- Correct prediction: Model predicts correctly without fallback.
- Fallback success: Model failed but fallback handled it correctly.
- Fallback failure: Model and fallback both failed.
- System error/crash: System stopped working.
    

Precision vs Recall tradeoff with concrete examples

In fallback and error handling, the tradeoff is between strict error detection and user experience. For example:

• If fallback triggers too often (high recall of errors), users may get many fallback messages, which can annoy them.
• If fallback triggers too rarely (high precision), some errors slip through and cause wrong outputs or crashes.

Good fallback systems balance this by catching most errors (high recall) but only when really needed (high precision), so users get smooth experience.

What "good" vs "bad" metric values look like for fallback and error handling

• Good:
  • Fallback success rate > 95%
  • Fallback failure rate < 2%
  • System error/crash rate < 1%
  • Low false fallback triggers (high precision)
• Bad:
  • Fallback success rate < 70%
  • High fallback failure or system crash rates
  • Fallback triggers too often causing user frustration
  • Errors silently ignored causing wrong outputs

Metrics pitfalls

• Accuracy paradox: High overall accuracy can hide many fallback failures if errors are rare.
• Data leakage: If fallback data leaks test info, metrics look better than real.
• Overfitting: Over-tuned fallback rules may fail on new errors.
• Ignoring user impact: Metrics may miss how fallback affects user trust and experience.

Self-check question

Your model has 98% accuracy but fallback success rate is only 50%. Is it good for production? Why not?

Answer: No, because even though accuracy is high, the fallback system fails half the time it is needed. This means many errors are not handled properly, risking wrong outputs or crashes. The system is not robust enough for real use.

Key Result

Fallback success rate and error handling robustness are key to ensure smooth user experience and system reliability.