ax-java-refine

Refine generated Java Ax outputs using evaluator feedback and optimizer APIs.

2.9k|186|Updated Feb 23, 2023

Search Tags:#refinement #java #generation #ax #evaluators #optimizers

Ready to run

npx skills add https://github.com/ax-llm/ax --skill ax-java-refine

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ax-java-refine
Source: https://github.com/ax-llm/ax/tree/main/website/static/java/.well-known/agent-skills/ax-java-refine
Command: npx skills add https://github.com/ax-llm/ax --skill ax-java-refine

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you improve generated Java code built with the Ax package by using reward signals, evaluator feedback, and optimizer-driven refinement instead of manual trial and error.

Core Features & Use Cases

Reward-Scored Refinement: Iterate on candidate outputs and choose better results using evaluator signals.
Optimizer-Backed Workflows: Use generated Java optimizer APIs when a dedicated refine helper is not available.
Java Package Guidance: Work with the generated dev.axllm:ax surface, package examples, and AxIR-backed manifests for correct native syntax.
Use Case: A developer wants to tune a Java generation pipeline, compare multiple outputs, and apply the best candidate using evaluator feedback and optimizer artifacts.

Quick Start

Ask the Skill to refine a Java Ax workflow using evaluator feedback and the generated dev.axllm:ax APIs.

Frequently Asked Questions about ax-java-refine

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I refine generated Java code using evaluator feedback?▼

You refine generated Java code by applying reward-scored refinement workflows that iterate on candidate outputs and select better results using evaluator signals. This replaces manual trial and error with optimizer-driven feedback loops.

What is reward-scored refinement for Java generation pipelines?▼

Reward-scored refinement is an iterative process that improves Java generation pipelines by comparing multiple candidate outputs and applying the best candidate based on evaluator feedback and optimizer artifacts.

Do I need generated dev.axllm:ax APIs to use optimizer-backed Java refinement?▼

Yes, you need the generated dev.axllm:ax APIs, optimizer engines, evaluator feedback loops, package examples, and AxIR-backed package truth to correctly perform optimizer-backed Java refinement.

Can I rerank Java Ax candidate outputs without a dedicated refine helper?▼

Yes, you can rerank Java Ax candidate outputs without a dedicated refine helper by using the generated Java optimizer APIs to guide the refinement and evaluation process.

What's the best way to tune a Java generation pipeline with optimizer artifacts?▼

The best way to tune a Java generation pipeline is using reward-scored refinement workflows that compare multiple outputs and apply the best candidate utilizing evaluator feedback and generated optimizer APIs.