arize-prompt-optimization

Optimize LLM prompts using Arize trace data and evaluation scores.

Updated May 5, 2026

Search Tags:#prompt engineering #prompt optimization #llm #evaluation #llm observability #trace data #arize

Ready to run

npx skills add https://github.com/ethan-rio/skills --skill arize-prompt-optimization-ethan-rio

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: arize-prompt-optimization
Source: https://github.com/ethan-rio/skills/tree/main/skills/arize/arize-prompt-optimization
Command: npx skills add https://github.com/ethan-rio/skills --skill arize-prompt-optimization-ethan-rio

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

LLM prompts deployed in production often underperform without data-driven refinement, and manual prompt tweaking is guesswork without access to real performance signals from traces, evaluations, and human annotations.

Core Features & Use Cases

Production Prompt Extraction: Pulls prompts directly from OpenInference-compliant LLM spans in Arize trace data, including structured chat messages and prompt templates.
Performance Signal Analysis: Aggregates evaluation scores, human annotations, and experiment results to identify specific failure patterns in prompt outputs.
Use Case: If your customer support chatbot is returning incorrect or poorly formatted answers, use this skill to analyze failure traces, generate an optimized prompt, and measure improvement via A/B experiments.

Quick Start

Use the arize-prompt-optimization skill to revise your underperforming production LLM prompt using your existing Arize trace data, evaluation scores, and human annotations.

Frequently Asked Questions about arize-prompt-optimization

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

FAQPage Schema

How do I optimize LLM prompts using production trace data?▼

You can optimize LLM prompts by analyzing production trace data, evaluation scores, and human annotations to identify failure patterns and generate revised prompt templates that improve output quality.

What is the best way to fix underperforming prompts in a production LLM application?▼

The best way to fix underperforming prompts is to use a data-driven optimization loop that analyzes real performance signals from OpenInference-compliant traces and human annotations instead of manual guesswork.

Do I need Arize trace data to use this prompt optimization process?▼

Yes, you need Arize experiment results and OpenInference-compliant trace data available to extract production prompts and aggregate evaluation scores for analysis.

How does prompt optimization handle existing template variables?▼

The prompt optimization process preserves existing prompt template variables while applying data-driven refinement to avoid overfitting to test data.

Can I use this to improve a customer support chatbot returning incorrect answers?▼

Yes, you can analyze failure traces from your chatbot, generate an optimized prompt, and measure output improvement via A/B experiments.

Why does manual prompt tweaking fail for production LLM applications?▼

Manual prompt tweaking fails because it relies on guesswork without access to real performance signals like evaluation scores, production traces, and human annotations to identify specific failure patterns.