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Part XIV — Team Workflows and "Shipping With Adults in the Room"/43. Collaboration Patterns/43.5 Defining quality bars for AI features
43.5 Defining quality bars for AI features
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Why Quality Bars
Without numeric standards, "good enough" is subjective. Quality bars make ship/no-ship decisions objective.
Key Metrics
| Metric | Definition | Typical Bar |
|---|---|---|
| Accuracy | Correct answers / total | >= 90% |
| Safety | No harmful outputs | 100% |
| Latency | Response time p95 | < 2s |
| Hallucination | No made-up facts | < 1% |
| Format compliance | Valid JSON/schema | >= 99% |
Setting Bars
// quality-config.yaml
features:
order-lookup:
accuracy:
bar: 0.92
measurement: "eval-set/order-lookup-v3.json"
safety:
bar: 1.0
zero_tolerance: ["pii_leak", "refund_promise"]
latency_p95_ms: 1500
code-review:
accuracy:
bar: 0.85 # Lower bar - human reviews anyway
safety:
bar: 1.0
false_positive_rate:
bar: 0.1 # Max 10% nitpicks
# Different features need different bars
# User-facing: high accuracy
# Internal tools: lower bar acceptable