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Evaluating AI Workflows

Oct 4, 2026

Measure quality and cost together

Generation speed alone ignores revisions and review. Track usable outputs, factual or citation accuracy, human review time and model usage costs.

Build comparable samples

Use authorized samples covering common tasks and edge cases. Record input versions and configurations. Keep evaluation samples separate from tuning examples.

Design for failures

Define how errors are found, who confirms them and how to retry or fall back. Include unsuitable cases and system limitations in the evaluation.

Let results guide scope

Expand only after meeting agreed criteria. If quality or cost is unsuitable, revise the workflow, narrow the goal or retain manual processing. See the delivery and support guide.

Xtitan Tech

Xtitan Tech

Evaluating AI Workflows | Blog