Unit 1 · Build AI features you can measure

Measure whether your AI feature improved

You pair realistic examples with a metric, then compare versions on the same evidence. This turns prompt tweaking into a repeatable improvement loop.

7 focused minutesNot started

Unit example (nearest native match)

See the idea in context

var qa = Ax.ax("question:string -> answer:string, confidence:number");

Keep a known answer
Each example records the behavior you want the program to reproduce.
Score one prediction
The metric returns 1 only when the predicted sentiment matches the example.
Compare on the same set
Reusing the dataset makes a new score meaningful instead of anecdotal.

Run itIn your own project

// Gradle (build.gradle):
implementation 'dev.axllm:ax:22.0.4'
// Maven (pom.xml):
<dependency>
  <groupId>dev.axllm</groupId>
  <artifactId>ax</artifactId>
  <version>22.0.4</version>
</dependency>

import dev.axllm.ax.Ax;

var llm = Ax.ai("openai", Map.of("apiKey", System.getenv("OPENAI_API_KEY")));
var classify = Ax.ax("review:string -> sentiment:class \"positive, negative, neutral\"");

Set OPENAI_APIKEY in your environment before running provider-backed code.

In the ax repo

From a clone of the ax repo:

npm run example -- java src/examples/java/generation/StructuredGenerationExample.java

Active practice

Show that you can use it

Answer 2 in a row to learn this · attempt 1

Keep exploring

See the idea in context

Show that you can use it

Source-backed follow-up