Unit 8 · Measure and improve AI quality

Compare better prompts without one fake winner

You let GEPA reflect on failures and change optimizable components. A Pareto frontier keeps honest tradeoffs between quality, cost, latency, and brevity visible.

AxGEPA12 focused minutesNot started

Unit example (nearest native match)

See the idea in context

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

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