Rate limiting and abuse prevention in Prompt Engineering / GenAI - Model Metrics & Evaluation

Metrics & Evaluation - Rate limiting and abuse prevention

Which metric matters for Rate limiting and abuse prevention and WHY

For rate limiting and abuse prevention, the key metrics are False Positive Rate and False Negative Rate. False positives mean blocking good users, which hurts user experience. False negatives mean letting bad users abuse the system, which causes harm. Balancing these is critical.

Precision and recall are also important: precision shows how many blocked users were truly abusive, and recall shows how many abusive users were caught. High recall prevents abuse, high precision avoids blocking good users.

Confusion matrix example

    |---------------------------|
    |           | Predicted     |
    | Actual    | Abuse | Good  |
    |-----------|-------|-------|
    | Abuse     | 90    | 10    |
    | Good User | 15    | 885   |
    |---------------------------|

    TP = 90 (abusive users correctly blocked)
    FN = 10 (abusive users missed)
    FP = 15 (good users wrongly blocked)
    TN = 885 (good users correctly allowed)
    Total = 1000
    

Precision vs Recall tradeoff with examples

If you set strict limits, you catch almost all abusers (high recall) but block many good users (low precision). This frustrates real users.

If you set loose limits, you block fewer good users (high precision) but miss many abusers (low recall), risking system abuse.

Example: A chat app wants to stop spam. High recall means catching most spammers but may block some normal users. High precision means blocking only real spammers but some spam may get through.

Good vs Bad metric values for this use case

Good: Precision around 90% or more and recall above 85%. This means most blocked users are truly abusive and most abusers are caught.

Bad: Precision below 50% means many good users blocked. Recall below 50% means many abusers slip through.

Common pitfalls in metrics

• Accuracy paradox: If abuse is rare, a model blocking no one can have high accuracy but is useless.
• Data leakage: Using future or leaked info inflates metrics but fails in real use.
• Overfitting: Model works well on training data but fails to generalize, causing poor real-world abuse detection.

Self-check question

Your abuse prevention model has 98% accuracy but only 12% recall on abusive users. Is it good for production?

Answer: No. The high accuracy is misleading because abuse is rare. The very low recall means it misses most abusers, so it won't effectively prevent abuse.

Key Result

Balancing high recall and precision is key to effective rate limiting and abuse prevention.