LLM Evaluations for Business Automation: A Simple Playbook

If an AI workflow touches customers or revenue, you need evals. Here’s a lightweight method to measure quality and prevent regressions.

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Define “good output” in one page

Write 10–30 real examples with expected outcomes. Include edge cases and failure modes.

Score what matters

Use a rubric: correctness, completeness, policy compliance, and whether the output is safe to execute.

Ship with monitoring

Log prompts, tool calls, and outcomes. Track cost and latency. Add fallback flows for uncertainty.