Test AI behavior on real examplesYou record realistic inputs, criteria, expected or forbidden actions, predictions, and traces. Use deterministic metrics when possible and judges when quality needs holistic review.cppacademyacademy/topics/evals-metrics-judgeswebsite/content-src/academy/course.mjsacademyTest AI behavior on real examples
You record realistic inputs, criteria, expected or forbidden actions, predictions, and traces. Use deterministic metrics when possible and judges when quality needs holistic review.
9 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);
Use a realistic input
The refund request represents the kind of task the agent will face.
Write the success rule
criteria explains that eligibility must be verified before action.
Record observable behavior
expectedActions lets the evaluation check tool selection, not only final prose.
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/optimization/axgen_optimization.cpp