Unit 3 · Build a reliable AI-powered feature

Recover from invalid model output

You let Ax validate fields and retry with specific correction feedback. Your app can still distinguish generation errors, provider failures, and cancellation.

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Unit example (nearest native match)

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var answer = Ax.ax("question:string -> answer:string");

Run the program normally
Most valid responses return through the usual typed path.
Catch the specific failure
AxGenerateError identifies a generation or validation failure you can report deliberately.
Preserve useful details
error.details gives logs and UI a concrete repair target.

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/BasicGenerationExample.java

Active practice

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