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

Worked example

See the idea in context

const result = await optimize(program, train, metric, { maxMetricCalls: 80, objectives: ['accuracy', 'brevity'] });

Run itIn your own project

npm install @ax-llm/ax

import { ai, ax } from '@ax-llm/ax';

const llm = ai({ name: 'openai', apiKey: process.env.OPENAI_APIKEY! });
const classify = ax('review:string -> sentiment:class "positive, negative, neutral"');

const result = await classify.forward(llm, {
  review: 'Useful and boring in the best way.',
});

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 -- typescript src/examples/typescript/optimization/axgen-optimization.ts

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