LLM features are easy to demo and hard to trust. Before shipping one into a real workflow, a team needs a small set of measurements that reflect how the feature will be used.

Task success

Start with the obvious question: did the system complete the task? For a support answer, that might mean citing the right policy. For an analysis tool, it might mean extracting the right fields and preserving the right caveats.

Task success should be measured against examples from the product, not generic benchmark prompts.

Failure cost

Some failures are annoying. Others are expensive. Separate formatting mistakes, missing context, wrong recommendations, policy violations, and irreversible actions. The mitigation should match the cost of the failure.

This is especially important for agents that can call tools.

Latency and cost

Users do not experience model quality in isolation. They experience quality, speed, and price together. A slow feature can feel broken even when the answer is correct.

Track latency by workflow stage: retrieval, model call, tool execution, and final rendering.

User correction patterns

Corrections are gold. When users edit an answer, rerun a prompt, reject a suggestion, or escalate to a human, the product is telling you where the model needs better context or tighter constraints.

The fastest teams turn those corrections into evaluation cases.

Confidence with evidence

For many product workflows, confidence should come from evidence rather than model tone. Show sources, intermediate checks, assumptions, and unresolved questions. A cautious answer with strong evidence is often better than a polished answer that hides uncertainty.