Evaluating AI Features in Mobile Apps: Metrics That Actually Matter
A practical evaluation stack for mobile AI features, from offline rubrics to production telemetry and rollback rules.
Alok Choudhary
Austin, TX
1 min read
Adding AI to a feature is easy. Knowing whether it actually improves user outcomes is the hard part.
I’ve been standardizing evaluation in three layers.
Layer 1: Offline scenario checks
- Curated prompt/context pairs from real product use cases.
- Rubrics for factuality, relevance, and actionable usefulness.
- Regression tracking by model/prompt/policy version.
Layer 2: Pre-release product validation
- Internal dogfood with structured feedback forms.
- Explicit red-team prompts for misuse and failure modes.
- UX review for uncertainty communication and user control.
Layer 3: Production telemetry
- Feature engagement with completion outcomes.
- User correction frequency and abandonment points.
- Latency and cost per successful user task.
Rollback conditions I enforce
- Spike in correction or dissatisfaction signals.
- Latency beyond interaction tolerance thresholds.
- Safety-policy breach indicators.
Evaluation is what turns AI from novelty to durable product capability.
Without it, teams optimize for demos. With it, teams optimize for users.