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Monitoring and release evaluation

Monitoring and release evaluation

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Connect quality signals with release decisions.

1. Telemetry boundaries

Record request outcome, model, prompt version and timing without logging raw receipts or identifiers unnecessarily. Separate product analytics from sensitive inference payloads.

2. Device and network cases

Test low connectivity, process recreation and foreground-background transitions. Simulated SDK errors are useful, but include at least one real protected integration test.

3. Release gates

Compare extraction accuracy and correction rate against a held-out set. Set thresholds before running the evaluation and document who can disable the feature.

Worked scenario

A prompt improves merchant extraction but increases total corrections. Segmenting results by field reveals why the aggregate success number is misleading.

Apply it

Create a release report covering accuracy, latency, protection checks, cost and rollback.

Check your understanding

Your rollout decision includes measured quality and an exercised fallback. Explain the decision and show evidence from your implementation or design. If you cannot demonstrate it yet, revisit the relevant section before continuing.

YOUR LEARNING JOURNEY

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Monitoring and release evaluation | Firebase AI Logic | Android Engineers