⚡Understanding the difference between observations and evaluations in AI testing, and how to demonstrate this rigor builds customer confidence.
Key Takeaways
- •Observations are qualitative insights from watching AI performance
- •Evaluations are quantitative measurements against defined success criteria
- •Start with observations to identify what matters, then create evals to measure it
- •Customers care about outcomes, not metrics—translate your testing into their value
- •Transparency about your testing process builds trust and becomes a competitive advantage
Understanding the Difference
Observations are what you notice during development. Your AI agent handles a call smoothly. It struggles with accents. It interrupts customers. These are qualitative insights that guide improvement.
Evaluations (Evals) are systematic measurements. You define success criteria, run standardized tests, and get quantifiable results. Does the agent correctly capture phone numbers 95% of the time? Does it complete bookings without human intervention?
Testing encompasses both—the structured process of observing behavior and evaluating performance against defined standards.
The Correct Order
Start with observations. Deploy your AI agent in controlled scenarios. Watch how it performs. Listen to calls. Note failure patterns. These observations reveal what matters.
Use observations to define evaluation criteria. If you notice the agent frequently misunderstands menu items, create an eval: "Correctly identify menu items from spoken requests—target 98% accuracy."
Run evaluations regularly. As you improve the system, evals provide objective evidence of progress. They catch regressions you might miss through casual observation.
Return to observations when evals fail. Numbers tell you what broke. Observations reveal why.
The Customer Value Problem
Here's the challenge: your observations and evals matter to you. They don't automatically matter to customers.
A customer doesn't care that your agent scores 94% on intent classification. They care that callers get their questions answered and appointments booked without frustration.
Demonstrating value requires translation. Instead of: "Our eval shows 97% accuracy on address capture," say: "97 out of 100 callers have their delivery addresses recorded correctly the first time—no callbacks needed."
Connect internal metrics to external outcomes. Failed evals mean missed appointments, frustrated customers, lost revenue. Passing evals mean smoother operations, happier customers, captured business.
Making Testing Visible
Smart teams show customers their testing process. Share example conversations—both successful and failed. Explain how you caught and fixed problems before they affected real callers. Demonstrate continuous improvement through before/after metrics.
This transparency builds trust. Customers see you're not just deploying AI and hoping for the best. You're systematically ensuring quality.
Conclusion
Observations guide development. Evals measure progress. Testing combines both into a systematic quality process. But the real skill is translating internal rigor into customer confidence. Show your work. Connect your metrics to their outcomes. Let testing become a selling point, not just a development practice.