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

auto qa = axllm::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

include(FetchContent)
FetchContent_Declare(axllm GIT_REPOSITORY https://github.com/ax-llm/ax GIT_TAG main SOURCE_SUBDIR packages/cpp)
FetchContent_MakeAvailable(axllm)
target_link_libraries(your_app PRIVATE axllm::axllm)

#include <axllm/axllm.hpp>

auto llm = axllm::ai("openai", axllm::object({{"apiKey", std::getenv("OPENAI_API_KEY")}}));
auto classify = axllm::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 -- cpp src/examples/cpp/generation/structured_generation.cpp

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