Trade extra model work for a better answerYou use refine() when one request should generate, critique, and improve candidates at runtime. It is separate from offline optimization and long-lived playbook learning.pythonacademyacademy/topics/refine-selectionwebsite/content-src/academy/course.mjsacademyTrade 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
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