Unit 1 · Build AI features you can measure

Measure whether your AI feature improved

You pair realistic examples with a metric, then compare versions on the same evidence. This turns prompt tweaking into a repeatable improvement loop.

7 focused minutesNot started

Unit example (nearest native match)

See the idea in context

from axllm import ai, ax

qa = ax('question:string -> answer:string, confidence:number')

Keep a known answer
Each example records the behavior you want the program to reproduce.
Score one prediction
The metric returns 1 only when the predicted sentiment matches the example.
Compare on the same set
Reusing the dataset makes a new score meaningful instead of anecdotal.

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/generation/structured.py

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