AI features are easy to demo and surprisingly hard to ship. The first version often looks good in a handful of examples, then quietly disappoints when it meets real inputs, edge cases, and changing model behavior.
The fix is not “more prompting” or “a better model.” The fix is an evaluation plan you can actually run repeatedly with limited time: a small scorecard, a small test set, and a lightweight habit of checking results before and after changes.
This post lays out a small-team approach that works for chat assistants, classifiers, extractors, summarizers, and “draft this email” style tools. It is intentionally practical and low ceremony. If you want more posts like this, browse the archive.
What you are evaluating (and what you are not)
Start by being specific about what the AI feature is responsible for. Most AI features are not “answer everything correctly.” They are “produce a useful draft,” “extract fields reliably,” or “route a request to the right bucket.” When teams skip this step, evaluations become vague and inconsistent.
A helpful framing is: what is the user’s job-to-be-done and what portion of that job the AI will handle. Your evaluation should measure that portion, not the entire workflow.
- In scope: output quality, safety boundaries relevant to your product, formatting consistency, coverage of required fields, and failure handling (for example “ask a clarifying question”).
- Out of scope: broader business outcomes that have long feedback loops (retention, revenue) and issues you cannot attribute to the AI output alone.
Also decide what “good enough” means. If the AI output is always reviewed by a human, you can accept more roughness than if the output is sent directly to customers.
Define success with a simple scorecard
You need a scorecard that a reviewer can apply quickly and consistently. Think “rubric,” not “vibes.” The scorecard should have 3 to 6 criteria, each with a clear definition.
For many AI features, a mix of binary checks (pass/fail) and graded checks (0 to 2, or 1 to 5) works best. Binary checks catch hard requirements. Graded checks capture quality that is hard to reduce to a single rule.
Example scorecard for an AI email drafter
- Factual alignment (0 to 2): Does the draft match the provided order details and policies?
- Actionability (0 to 2): Does it clearly tell the customer what happens next?
- Tone (0 to 2): Friendly and professional, no scolding, no excessive apologies.
- No sensitive leakage (pass/fail): No internal notes, no personal data beyond what the agent provided.
- Format (pass/fail): Uses the required structure (greeting, body, closing) and includes the ticket ID if needed.
Pick criteria that reflect real user pain. If “tone” rarely matters but “missing required fields” breaks workflows, weight your effort accordingly.
Key Takeaways
- Define the AI feature’s responsibility clearly before you measure anything.
- Use a small, explicit scorecard so different reviewers agree.
- Keep a compact test set and run it after every meaningful change.
- Track regressions by category, not just an overall average.
Build a small test set that stays representative
Your test set is a curated collection of inputs that represent what users actually do. It does not need to be huge. For small teams, 30 to 80 examples is often enough to start catching regressions, as long as the set is diverse.
A good test set covers:
- Happy paths: straightforward cases that should be near-perfect.
- Common messy inputs: missing details, typos, short messages, long messages.
- Edge cases: unusual product types, policy exceptions, rare categories.
- Boundary cases: requests the AI should refuse or redirect (for example “I need a refund for an order older than the allowed window”).
Keep each example self-contained so you can replay it later. If your feature relies on context (account tier, order history, plan limits), include that context as part of the test item.
A compact “eval case” structure you can reuse
This is not code you must implement, just a conceptual shape that keeps cases consistent:
{
"caseId": "support-email-042",
"input": { "customerMessage": "...", "order": { "status": "delivered", "date": "..." } },
"policyContext": "refund-window-30-days",
"expectedBehavior": ["confirm-order", "offer-next-step", "no-policy-invention"],
"riskTags": ["refund", "tone-sensitive"]
}Even if you store this in a spreadsheet instead of a file, the fields help you compare apples to apples.
Run evaluations in three modes
Small teams need evaluations that fit into normal work, not a separate research project. A practical pattern is to run the same test set in three modes, depending on what you are trying to learn.
1) Quick pre-merge spot check
Before shipping a prompt change, model swap, or tool change, run a fast check on a handful of high-risk cases (for example 10). This is your “smoke test.” It catches obvious formatting breakage, tone shifts, and tool failures.
Keep this lightweight: one person, one short checklist, 10 to 15 minutes. The goal is preventing embarrassing regressions, not producing a perfect metric.
2) Scheduled regression run
On a predictable cadence (weekly or per release), run the full test set and record scores. You are looking for:
- Regression: something got worse compared to the last run.
- Drift: behavior changes even though you did not change your code (common with model updates or tool behavior changes).
- Coverage gaps: categories where you do not have enough cases.
Keep history. A single run is a snapshot. A series of runs tells you whether improvements stick.
3) Targeted deep-dive after incidents
When a real user reports a bad output, do not just fix that one prompt. Add a case to the test set that captures the failure mode, tag it, and run it anytime you change related logic. Over time, your test set becomes a “memory” of past mistakes.
Common mistakes to avoid
- Only measuring the average: An overall score can rise while a critical category gets worse. Break results down by risk tags or scenario types (refund, cancellation, sensitive tone).
- Letting the test set rot: If your product changes, your test set must change too. Retire cases that no longer represent reality.
- Overfitting to the test set: If you tune only to the cases you have, you might make real-world behavior worse. Add new cases regularly from real usage (with appropriate privacy handling).
- Unclear reviewer instructions: If two reviewers score the same output differently, your rubric is too vague. Rewrite the definitions until reviewers agree most of the time.
- Ignoring “non-answer” quality: Many failures are not wrong answers, but bad failure behavior (confident nonsense, refusal when it should ask a question, or missing a required field).
When not to do this
A lightweight evaluation plan is helpful, but it is not always the next best step.
- If the feature is not stable enough to define (you are still debating what it should do), focus on product definition first, then evaluate.
- If you cannot replay inputs (you lack a way to capture the context needed to reproduce outputs), invest in basic observability and logging before formal eval runs.
- If the risk is high and the impact is severe (for example, outputs that could materially harm users), a small-team plan may be insufficient. You likely need stronger controls, more review, and more rigorous testing.
In other words: use this approach when you can iterate, measure, and improve safely. If you cannot, fix the prerequisites first.
A practical checklist you can copy
Use this as a starting point for your own evaluation habit:
- Write a one-sentence responsibility statement for the AI feature (what it does, what it does not do).
- Define 3 to 6 scorecard criteria, including at least one hard pass/fail check.
- Collect 30 to 80 representative cases (happy path, messy, edge, boundary) and tag them.
- Decide how you will review: one reviewer, two reviewers for high-risk categories, or rotating ownership.
- Run a spot check before shipping and a regression run on a cadence.
- Record results and keep history, including notes on failures by category.
- Turn incidents into test cases so you do not repeat the same mistake.
Conclusion
You do not need a massive dataset or a research team to evaluate AI features. You need a clear definition of success, a small test set that matches reality, and a habit of rerunning the same checks after changes. Over time, this turns “AI feels unpredictable” into “AI is a component we can improve.”
FAQ
How many test cases do we need to start?
Start with 30 to 50 cases if you are short on time. Prioritize diversity and risk coverage over volume. Add new cases as you learn, especially from real failures.
Should we use human review, automated metrics, or both?
For many teams, human review is the fastest path to signal, especially early. Automated checks are great for hard requirements (formatting, required fields, length limits). Combining both usually works best.
How do we keep reviews consistent across people?
Make the rubric concrete, include examples of what earns a 0, 1, or 2, and do occasional calibration sessions where reviewers score the same outputs and reconcile differences.
What if model outputs change even when we did not deploy anything?
That is exactly why a scheduled regression run helps. Treat it like dependency drift. If behavior changes, decide whether to accept it, mitigate it (prompt or tool changes), or add guardrails for the affected categories.
Where should we store the test set and scores?
Anywhere your team will actually maintain it: a versioned file, a simple internal tool, or even a spreadsheet. The important part is replayability, history, and clear ownership.