Unit 3 · Build a reliable AI-powered feature

Choose better results and keep context

You can keep chat context, sample several candidates, select one result, cache responses, and observe steps. Add each option only when your feature needs that control.

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Unit example (nearest native match)

See the idea in context

auto answer = axllm::ax("question:string -> answer:string");

Carry relevant memory
mem supplies conversation context owned by your application.
Request candidates
sampleCount asks for three possible results instead of one.
Select with a rule
resultPicker turns extra samples into a deliberate quality choice.

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/generation/basic_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