Unit 8 · Measure and improve AI quality

Improve how an agent chooses and acts

You evaluate the whole agent pipeline and tune its actor or responder. Good task records exercise tool choice, clarification, delegation, and final quality.

optimize()11 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

package main

import (
    "context"
    "fmt"
    "os"

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

func main() {
    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)
    result, err := classify.Forward(context.Background(), client, map[string]axllm.Value{
        "review": "Useful and boring in the best way."
    }, nil)
    if err != nil {
        panic(err)
    }

    fmt.Println("sentiment:", result.(map[string]axllm.Value)["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 -- 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

Keep exploring

See the idea in context

Show that you can use it

Source-backed follow-up