Unit 8 · Measure and improve AI quality

Trade extra model work for a better answer

You use refine() when one request should generate, critique, and improve candidates at runtime. It is separate from offline optimization and long-lived playbook learning.

refine()8 focused minutesNot started

Unit example (nearest native match)

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let engine = AxGEPA::new(reflection_client, json!({}));
let result = engine.optimize(request, evaluator);

Run itIn your own project

cargo add axllm

use axllm::{ai, ax, AxResult};
use serde_json::json;

fn main() -> AxResult<()> {
    let mut llm = ai("openai", json!({"apiKey": std::env::var("OPENAI_API_KEY")?}))?;
    let mut classify = ax("review:string -> sentiment:class \"positive, negative, neutral\"")?;
    let result = classify.forward(&mut llm, json!({
        "review": "Useful and boring in the best way."
    }))?;

    println!("sentiment: {}", result["sentiment"].as_str().unwrap_or_default());
    Ok(())
}

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 -- rust src/examples/rust/optimization/axgen_optimization.rs

Active practice

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Answer 2 in a row to learn this · attempt 1

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Source-backed follow-up