ANSWER

“Good” for an AI feature is not just accuracy. “Good” means the feature delivers the intended outcome reliably, safely, and consistently within defined boundaries—and you can prove it.

What “good” looks like (the BA checklist)

A well-defined AI feature typically has targets in these areas:

1) Outcome (value)

• What business outcome improves (time saved, errors reduced, conversion increased)?
• What metric proves it?

2) Quality

• Accuracy (or usefulness) at an agreed threshold
• Consistency (similar inputs → similar outputs)
• Latency (response time)
• Cost (especially for AI inference)

3) Safety & compliance

• No prohibited content
• No leakage of sensitive data (PII, credentials)
• No unsafe actions (especially if the AI can act in systems)

4) Trust & transparency

• Users can understand why it produced an output (explanations, sources, confidence)
• The system handles uncertainty gracefully (“I’m not sure, here’s what to do next”)

5) Operability

• Monitoring in place (drift, anomalies, failure rates)
• Rollback/kill switch criteria defined

How you test it (practitioner approach)

Testing AI features blends traditional testing with evaluation:

A) Test with representative scenarios

• Build a test set of real-world cases (including edge cases)
• Include “hard cases” and “red team” attempts (misuse, injection, sensitive data prompts)

B) Define measurable acceptance thresholds
Examples:

• “At least 90% of outputs meet rubric score ≥ 4/5”
• “High-risk actions require human approval 100% of the time”
• “No PII appears in outputs in the test set”
• “Confidence below X triggers escalation”

C) Test behavior under uncertainty

• What does it do when data is missing?
• Does it hallucinate or does it ask for clarification?
• Does it choose safe defaults?

D) Monitor after release
Because AI behavior can change, “good” must be maintained:

• track drift,
• review exceptions,
• adjust guardrails and evaluation sets.