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. go academy academy/topics/refine-selection website/content-src/academy/course.mjs academy Trade extra model work for a better answer
ax
Go github.com/ax-llm/ax/packages/go
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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

engine := axllm.NewAxGEPA(reflectionClient, nil)
result := engine.Optimize(request, evaluator)
Run itIn your own project
go get github.com/ax-llm/ax/packages/go

import axllm "github.com/ax-llm/ax/packages/go"

client := axllm.NewAI("openai", map[string]axllm.Value{"apiKey": os.Getenv("OPENAI_API_KEY")})
classify := axllm.NewAx("review:string -> sentiment:class \"positive, negative, neutral\"", nil)

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 -- go src/examples/go/optimization/axgen_optimization.go
Active practice

Show that you can use it

Answer 2 in a row to learn this · attempt 1
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