You do not need a data science department to know if your AI is helping. You need clear goals, simple tests, and a routine for checking results. Treat AI like any other product feature.

Define success in plain language

Pick one outcome that matters and write it down.

• Reduce time to draft a response
• Increase first contact resolution
• Improve search answer acceptance
• Cut manual handoffs per ticket

Attach a number and a time frame. For example, improve answer acceptance from 60 percent to 80 percent in four weeks.

Create golden test cases

Build a small set of real examples that never changes.

• 20 to 50 representative prompts or questions
• Known good answers written by your team
• Edge cases and common traps
  Run every new prompt change against this set and record pass or fail.

Score what users actually see

Collect simple quality signals from production.

• Thumbs up or down on each answer
• Time to first useful answer
• Edit distance from draft to final message
• Escalation rate to human experts

Use weekly trend lines, not one day spikes.

Check grounding and citations

If your AI cites sources, verify them.

• Each answer must link to a real document
• Links must open and match the claim
• Flag answers with missing or broken citations

Reject answers that cannot prove where the facts came from.

Watch for safety and compliance

Block obvious risks early.

• Redact PII and secrets in logs
• Limit access to internal content by role and team
• Add a rate limit per user and per integration
• Keep prompts and responses for audit only as long as needed

Build a lightweight evaluation loop

Keep it simple and repeatable.

• Daily: scan errors and top negatives
• Weekly: review quality metrics with owners
• Biweekly: update golden tests and fix bad prompts
• Monthly: decide to expand, pause, or roll back

Use small experiments

Change one thing at a time.

• New model, same prompts
• New prompts, same model
• Retrieval settings A vs B
  Ship to a small group behind a feature flag before rolling out.

Cost and latency belong in quality

Great answers that arrive too slowly or cost too much still fail.

• Track cost per action
• Track p50 and p95 latency
• Set budgets and alerts
  
  Quality is not a mystery. With clear goals, golden tests, simple user signals, and regular reviews, any team can evaluate AI with confidence. If you want a practical scoring plan and dashboards you can run without a data team, ping us at Code Scientists.