Most AI workflow failures in week one are not surprises. They are predictable. The team just never wrote them down before shipping.
An evaluation pack changes that. It is a structured set of representative inputs, expected outputs, and pass/fail criteria assembled before a workflow goes live. It is not a unit test suite. It is not a benchmark against a public dataset. It is a small, deliberate collection of cases that reflects your actual production environment.
Building one takes a few hours. Skipping one costs days.
What an Evaluation Pack Actually Is
A unit test checks whether code runs correctly. A benchmark measures performance against a standardized task. An evaluation pack does neither of those things.
It answers one question: does this workflow behave correctly on the inputs it will actually receive?
The pack contains three things for each case:
- Input: a realistic prompt, record, or document the workflow will process
- Expected output: what correct behavior looks like — a classification label, a structured field, a summary with specific required elements
- Pass/fail criterion: a concrete rule for deciding whether the output qualifies
The criterion is the part most teams skip. "Good enough" is not a criterion. "Contains the account name and a next-step recommendation" is.
How to Select the Right 15–20 Cases
Fifteen to twenty cases is the right size for a pre-deployment pack. Fewer and you miss real variance. More and the pack becomes expensive to maintain and slow to run.
The goal is to cover 80% of production variance with those cases. Here is how to find them.
Start with the clean case
One or two cases where every required field is present and the input is unambiguous. This is your baseline. If the workflow fails here, nothing else matters.
Add edge inputs
Inputs that are technically valid but unusual. A company name with special characters. A date field in an unexpected format. A free-text field that is 10x longer than typical. These expose brittle parsing and prompt sensitivity.
Add missing-field cases
Real production data is incomplete. Pick the three fields most likely to be absent and build one case for each. The workflow needs a defined behavior when those fields are missing — not a crash, not a hallucinated value.
Add ambiguous classifications
If the workflow makes any classification decision — industry, intent, priority, sentiment — find two or three inputs that sit on the boundary between categories. These are the cases where the model's judgment matters most and where drift shows up first.
Add known adversarial patterns
Every domain has them. For outbound sales workflows, it might be a contact record where the job title is "CEO" but the company has two employees. For document processing, it might be a PDF where the relevant data is in a table footer. Collect these from your team before deployment, not after.
Running the Pack as a Deployment Gate
An evaluation pack only works if it is required. If it is optional, it will be skipped under deadline pressure — which is exactly when it matters most.
The integration is simple. Add the eval run as a checklist item before any workflow moves to production. The checklist item has two fields:
- Baseline score: the percentage of cases the workflow passed on the first run
- Minimum threshold: the score required to ship
Set the threshold before you run the pack. Setting it after you see the score defeats the purpose.
A reasonable starting threshold for most workflows is 85%. That means 17 of 20 cases pass. The three failures get documented — not fixed necessarily, but documented — so the team knows what the workflow does not handle yet.
What to do with failures
Not every failure blocks deployment. Some failures reveal edge cases the workflow was never designed to handle. Those get logged as known limitations.
Some failures reveal that the workflow is wrong on cases it should handle. Those block deployment.
The distinction is made before the pack runs, when the team decides which cases are in-scope and which are out-of-scope. That decision is part of building the pack, not part of interpreting results.
Rerunning the pack
The pack is not a one-time artifact. Run it again after any prompt change, model update, or upstream data schema change. The baseline score from the first run becomes the floor. If a subsequent run scores lower, something regressed.
This is the feedback loop that keeps a workflow stable over time without requiring a full audit every time something changes.
The Operational Payoff
Teams that build evaluation packs before deployment find production issues in hours, not days. They also have a concrete record of what the workflow was tested against — useful when a stakeholder asks why a specific case was handled a certain way.
The pack does not guarantee a perfect workflow. It guarantees a workflow that was checked against real conditions before it touched real data.
That is a different starting point than most teams have.
If you are building or auditing an AI workflow and want a second set of eyes on your evaluation approach, start a conversation →