Fun fact about AI evals: They're just systematic data analytics for your AI app.

Let me explain...

You look at the data flowing through your system.

You define what “good” means.

You measure it.

You iterate.

Without evals, every prompt change is a coin flip.

With evals, you have a feedback signal.

Here are the 3 core scenarios where AI evals play a central role:

1/ Optimization (Development)

During development, evals help you improve what you’re building.

New retrieval strategy?

New prompt structure?

New tool?

You must measure if it's better than what exists.

You want to know: “Did this change improve quality?”

2/ Regression (Development)

Every code change risks breaking something that used to work.

Evals act like unit and integration tests for behavior.

You modify the retrieval pipeline...

Tweak the agent loop...

Refactor tool calls.

To ensure:

• Previous capabilities still work

• Edge cases remain covered

• Quality doesn’t silently degrade

Conceptually, this is no different from classic software testing.

Except now you're testing non-deterministic systems.

3/ Product Monitoring

Once your system is live, the real world starts stress-testing it.

Inputs drift.

New failure modes appear.

Users behave differently than your evals dataset.

But you still need your AI to provide value.

Production evals help detect:

• Behavioral drift

• Quality degradation

• Unexpected usage patterns

Without them, you only find out when customers complain.

There are also 2 complementary signals to complete the picture:

1. User Feedback. This is the highest-quality signal you can get. It bypasses your curated datasets entirely.

2. A/B Testing. This validates improvements on real traffic.

Together, they connect offline evaluation with real-world performance.

Here’s the gist:

Stop vibe-checking your AI Apps.

AI evals are not an add-on.

They are the control system for your application.

Optimization improves features.

Regression protects stability.

Production monitoring protects reputation.

User feedback reveals blind spots.

A/B testing validates reality.

I dive deeper into this in Lesson 1 of our AI Evals & Observability series in Decoding AI Magazine.

Read it here: decodingai.com/p/integr…