Unit 8 · Measure and improve AI quality

Improve a generator or workflow with data

You tune a generator or workflow from examples and a metric. Bound the budget, keep validation data separate, and apply the returned artifact through the program API.

optimize()10 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

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Answer 2 in a row to learn this · attempt 1

Keep exploring

See the idea in context

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Source-backed follow-up