Compare better prompts without one fake winnerYou let GEPA reflect on failures and change optimizable components. A Pareto frontier keeps honest tradeoffs between quality, cost, latency, and brevity visible.cppacademyacademy/topics/gepa-pareto-artifactswebsite/content-src/academy/course.mjsacademyCompare 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
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