Your AI Workflow Is Not Ready Until It Passes Ten Real Tests
A practical evaluation method for small teams that need proof before automation touches real clients, schedules, or records.
A successful demo is persuasive. It is not proof. One polished result tells you the workflow worked once, under friendly conditions, while everyone was watching.
Dear Suzannah
Question: How do I know an AI workflow is ready?
Answer: Define success, run representative and difficult cases, score the results, and require every critical case to pass.
Here’s the deal
A useful evaluation checks normal work, missing information, conflicting instructions, unusual requests, sensitive data, and situations that require a person. The cycle is simple: specify, measure, improve, and retest.
What this actually helps you do
Catch failures early
Find wrong assumptions and weak escalation before customers do.
Improve instructions
Use evidence to change the prompt, source files, or approval step.
Measure value
Compare speed, accuracy, correction time, and completion against the old process.
The niche use case
A consulting firm uses AI to turn discovery-call notes into a follow-up brief with decisions, tasks, owners, deadlines, and open questions. It builds ten sanitized cases from previous meetings and compares the drafts with approved final briefs.
Three benefits
- Fewer repeated corrections.
- Faster employee trust and adoption.
- Clearer evidence of whether the workflow creates net value.
Step-by-step
Define the job
Write the purpose in one sentence.
Define what good means
Choose measurable criteria for accuracy, completeness, tone, and escalation.
Build ten cases
Use representative real work, not only the cleanest example.
Include difficult cases
Add missing dates, conflicting notes, unclear ownership, sensitive data, and one case that should stop.
Create expected results
Use approved examples or a scoring guide.
Run cases consistently
Use the same settings, sources, and instructions.
Score each output
Mark pass, partial, or fail and separate serious errors.
Study patterns
Find repeated causes instead of fixing outputs one at a time.
Change one thing
Adjust one component, then rerun the same cases.
Set the launch threshold
Require every critical case to pass.
Tips and tricks
- Use real work whenever possible.
- Keep a fixed test set for comparison.
- Weight serious errors more heavily than formatting.
- Measure review time, not only generation time.
- Retest after changes to models, instructions, tools, or sources.
Common mistakes
- Testing only the example used to build the prompt.
- Using vague criteria such as looks professional.
- Ignoring cases where the AI should stop.
- Changing several parts before retesting.
- Launching because the average score looks good while one critical failure remains.
Human review checklist
- The purpose is specific.
- Success criteria reflect the business outcome.
- The ten cases represent real work.
- Difficult and stop-condition cases are included.
- Expected results were approved by a knowledgeable person.
- Serious errors are weighted separately.
- Review time is measured.
- A person approves the launch threshold.
How to measure success
- Critical accuracy.
- Required information completed.
- Claims traceable to sources.
- Escalation accuracy.
- Human review time.
- Net time saved.
- Failure severity.
FAQ
Are ten cases enough?
They are enough for a strong first screen. Add cases as new failures appear.
Can AI grade the outputs?
Yes, but compare automated grading with human judgment.
Should every error count equally?
No. An invented deadline matters more than awkward formatting.
Glossary
Evaluation: A repeatable test against defined expectations.
Test case: A representative input and expected behavior.
Golden set: Trusted examples used for comparison.
Regression: A change that causes a previous success to fail.
Sources and further reading
- OpenAI: How evals drive the next chapter in AI
- NIST: Generative AI Evaluation Program
- Anthropic: Demystifying evals for AI agents
Practical closing note
Do not launch because the demo looked good. Launch when the workflow has survived the cases that usually make real work messy.
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