Unit 8 · Measure and improve AI quality

Compare better prompts without one fake winner

You let GEPA reflect on failures and change optimizable components. A Pareto frontier keeps honest tradeoffs between quality, cost, latency, and brevity visible.

AxGEPA12 focused minutesNot started

Unit example (nearest native match)

See the idea in context

auto engine = axllm::AxGEPA(reflectionClient, axllm::object({}));
auto result = engine.optimize(request, evaluator);

Run itIn your own project

cmake_minimum_required(VERSION 3.20)
project(ax_quick_start LANGUAGES CXX)
set(CMAKE_CXX_STANDARD 17)

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

add_executable(quick_start quick_start.cpp)
target_link_libraries(quick_start PRIVATE axllm::axllm)

#include <axllm/axllm.hpp>
#include <cstdlib>
#include <iostream>

int main() {
  auto llm = axllm::ai("openai", axllm::object({{"apiKey", std::getenv("OPENAI_API_KEY")}}));
  auto classify = axllm::ax("review:string -> sentiment:class \"positive, negative, neutral\"");
  auto result = classify.forward(*llm, axllm::object({
    {"review", "Useful and boring in the best way."}
  }));

  auto sentiment = axllm::Core::get(result, "sentiment");
  std::cout << "sentiment: " << std::get<std::string>(sentiment.data) << "\n";
}

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/optimization/axgen_optimization.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