Verify Whether an AI Workflow Actually Works

A small evaluation set, explicit rubric, failure log, and evidence-backed decision to adopt, revise, or stop the workflow.

Anyone deciding whether a prompt, skill, agent, or automation is dependable enough for recurring use.

• The promised outcome
• Representative normal cases
• At least one edge and one failure case
• A baseline manual method
• A scoring rubric
• Human review time and correction cost

1. Define the claim

   Write what 'works' means as an observable artifact or decision. Separate technical execution, output quality, and business usefulness.

2. Build the evaluation set

   Use a small representative set containing normal, incomplete, ambiguous, adversarial, and out-of-scope cases. Keep private production data out unless explicitly authorized.

3. Create the rubric before running

   Score factual support, completeness, instruction following, safety, usability, and correction effort. Weight the dominant failure mode most heavily.

4. Run the baseline and workflow

   Compare against the current manual or simpler method. Record inputs, versions, outputs, latency, cost, and human interventions.

5. Inspect failures

   Classify each failure by cause, detectability, consequence, and recovery. A workflow that fails visibly may be safer than one that produces plausible errors.

6. Make the adoption decision

   Adopt only for the cases demonstrated. Specify monitoring, review gates, rerun triggers, and the evidence that would suspend the workflow.

• An evaluation table
• Synthetic fixtures
• A baseline artifact
• A rubric
• Run receipts or logs

• Workflow owner approves the success claim and rubric
• Data owner approves evaluation inputs
• Qualified reviewer scores high-stakes outputs
• Process owner approves adoption scope and monitoring

• If the success claim cannot be observed, rewrite it before testing
• If a failure is consequential and hard to detect, keep a human gate or reject the workflow
• If the workflow only beats a straw-man baseline, run a realistic comparison
• If model, prompt, skill, or tool version changes materially, rerun the relevant evaluation

• □The evaluation covers normal, edge, failure, and out-of-scope cases
• □The rubric existed before outputs were judged
• □A realistic baseline is included
• □Human correction time and severity are recorded
• □Failures have causes and recovery actions, not just scores
• □The adoption claim is limited to demonstrated cases
• □A suspension or reversal condition is defined

Design an honest evaluation for this AI workflow.

Workflow: [prompt, skill, agent, or automation]
Promised outcome: [observable result]
Current baseline: [manual or simpler method]
Users and stakes: [who relies on it and cost of error]
Available test cases: [describe]
Dominant failure mode: [what matters most]

Create: a precise success claim, representative synthetic evaluation set, weighted rubric, baseline comparison, run log fields, failure taxonomy, human-effort measurement, adoption thresholds, monitoring plan, and suspension condition. Include normal, incomplete, ambiguous, adversarial, and out-of-scope cases. Do not use a successful run status or model confidence as proof.

Synthetic example

Scenario

A meeting-summary prompt is proposed for weekly leadership meetings.

Input

Five synthetic transcripts: clear decisions, no decisions, conflicting owners, sensitive sidebar, and poor transcription. Baseline is a human note-taker.

Result

The prompt passes decision extraction but invents owners in two cases and leaks the sensitive sidebar. The decision is revise, not adopt. Owner invention becomes a hard-fail criterion; privacy filtering and a human approval gate are required before retest.