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

from axllm import AxGEPA

engine = AxGEPA(reflection_client)
result = engine.optimize(request, evaluator)

Run itIn your own project

pip install axllm

import os
from axllm import ai, ax

llm = ai('openai', api_key=os.environ['OPENAI_API_KEY'])
classify = ax('review:string -> sentiment:class "positive, negative, neutral"')
result = classify.forward(llm, {
    'review': 'Useful and boring in the best way.',
})

print('sentiment:', result['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 -- python src/examples/python/optimization/axgen-optimization.py

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