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. python academy academy/topics/refine-selection website/content-src/academy/course.mjs academy Trade extra model work for a better answer
ax
Python axllm
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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)

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

from axllm import ai, ax

llm = ai('openai', api_key=os.environ['OPENAI_API_KEY'])
classify = ax('review:string -> sentiment:class "positive, negative, neutral"')

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
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