What uptime/accuracy standards do you set for AI-driven features?
By Alex TaylorPosted: 27/04/2025
Tags: AI Performance, Quality Assurance, Production
If you launch a feature powered by an LLM, how reliable does it need to be to not embarrass the team? Are there benchmarks people are using?
Upvotes: 41
Downvotes: 0
Comments: 5
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I found that defining clear failure modes and fallback strategies is critical. We maintain a "model performance dashboard" that flags when accuracy drops below our baseline.
By: User #8
We've implemented a fallback system using multiple LLMs - if the primary model fails or gives low confidence scores, we automatically retry with a secondary model. It's improved our overall reliability.
By: User #19
We treat AI features similar to any other API dependency with SLAs - we target 99.5% uptime but accept some degradation in quality during unusual traffic patterns.
By: User #13
Measuring accuracy is the real challenge. We ended up creating an evaluation dataset that our product team regularly updates with new examples.
By: User #8
We set an expectation of 95% accuracy for critical paths and 85% for non-essential features. Anything below that gets human-in-the-loop fallbacks.
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