Evaluating LLM Systems in Production: From Implicit Signals to Safe Experiments
Show a practical way to move from guessing about LLM quality to measuring it using logs, labels, and simple experiments.
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Show a practical way to move from guessing about LLM quality to measuring it using logs, labels, and simple experiments.
How to design SaaS systems where many tenants share the platform but don't share failure modes, noisy neighbors, or data.
How to design web services that slow down gracefully instead of crashing when traffic or downstream latency spikes.
How to keep AI models on devices useful and safe after you ship them. Practical patterns for detecting drift, retraining, and safely rolling out updates to edge AIoT fleets.
How to train useful AI models across many devices without pulling all the raw sensor data into the cloud.
How to get thousands of devices securely online without manual setup, stickers, or spreadsheets. A complete guide to zero-touch provisioning for IoT fleets.
How to push firmware safely to thousands of devices without bricking them or waking the on-call team at 3 a.m.
How to design AI-powered devices that assume nothing is trusted: not the network, not the model, not even the local firmware.
How to ship new AI models to thousands of flaky, low-power devices without bricking them or breaking behavior.
How to move from one big shared cluster to multiple self-contained cells that limit incident impact and isolate noisy neighbors.