Unit 2 · Make AI outputs predictable

Reuse the same contract everywhere

You keep a field’s meaning stable as it moves through generators, tools, flows, agents, and events. That makes composed systems easier to validate and evaluate.

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Unit example (nearest native match)

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var sig = Ax.s("email:string -> priority:class \"high, normal, low\"");

Name the capability
lookup gives the model and traces a stable tool name.
Type the boundary
ticketId and the JSON result use the same field vocabulary as Ax programs.
Keep execution in your app
The handler owns the real lookup and returns the declared result.

Run itIn your own project

// Gradle (build.gradle):
implementation 'dev.axllm:ax:24.0.15'
// Maven (pom.xml):
<dependency>
  <groupId>dev.axllm</groupId>
  <artifactId>ax</artifactId>
  <version>24.0.15</version>
</dependency>

import dev.axllm.ax.Ax;
import java.util.Map;

public class QuickStart {
  public static void main(String[] args) throws Exception {
    var llm = Ax.ai("openai", Map.of("apiKey", System.getenv("OPENAI_API_KEY")));
    var classify = Ax.ax("review:string -> sentiment:class \"positive, negative, neutral\"");
    var result = classify.forward(llm, Map.of(
      "review", "Useful and boring in the best way."
    ));

    System.out.println("sentiment: " + result.get("sentiment"));
  }
}

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

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