Unit 3 · Build a reliable AI-powered feature

Choose better results and keep context

You can keep chat context, sample several candidates, select one result, cache responses, and observe steps. Add each option only when your feature needs that control.

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Unit example (nearest native match)

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answer := axllm.NewAx("question:string -> answer:string", nil)

Carry relevant memory
mem supplies conversation context owned by your application.
Request candidates
sampleCount asks for three possible results instead of one.
Select with a rule
resultPicker turns extra samples into a deliberate quality choice.

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/generation/basic_generation.go

Active practice

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Answer 2 in a row to learn this · attempt 1

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