Measuring and Iterating
In AI products, the metrics that matter are the ones nobody taught you.
Page views and signups tell you nothing about AI product health. You need to measure output quality, user trust, and whether the AI is actually solving the problem.
What you'll learn
- The AI-specific metrics that predict success or failure
- How to build a feedback loop that improves your AI over time
- When to optimize prompts vs. when to change the approach
- Using analytics to find your product's "aha moment"
AI Metrics That Actually Matter
Output acceptance rate: What percentage of AI outputs do users accept without editing? If it's below 60%, your AI isn't good enough yet. If it's above 90%, your users might be blindly accepting everything — which is a different problem.
Edit depth: When users do edit AI output, how much do they change? Light edits (fixing a word, adjusting tone) mean the AI is close. Heavy rewrites mean the AI is fundamentally missing the mark.
Return rate: Do users come back for a second, third, tenth time? First-use "wow" is easy. Repeated use means the product delivers consistent value. Track day-1, day-7, and day-30 retention separately.
Cost per successful output: Not cost per query — cost per output the user actually kept. If users need 3 regenerations to get something usable, your true cost is 3x what you think.
The AI Product Health Dashboard
Healthy: 70%+ acceptance rate, 3+ sessions/week, edit depth <20%, cost/output stable
Warning: 50-70% acceptance, declining sessions, edit depth 20-50%, cost/output rising
Critical: <50% acceptance, one-and-done users, heavy rewrites, cost/output unsustainable
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