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Prompt Hub optimization task configuration with dataset, experiment, columns, and meta-prompt model

Start a prompt optimization task from Prompt Hub

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What is prompt optimization?

Prompt Learning is Arize AX’s automated prompt-optimization path — an LLM reads your current prompt, evaluation feedback, and examples, then proposes a revised prompt that better matches your criteria. Same engine, exposed through the UI, SDK, Alyx, and Skills. For the conceptual frame — including how Prompt Learning compares to manual iteration and conversational refinement with Alyx — see Optimizing prompts.
Let Alyx optimize it for you. Press Cmd+L (macOS) or Ctrl+L (Windows/Linux) to open Alyx in the Playground and try: “Optimize this prompt to reduce hallucinations”

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Workflow

Use the Arize skills plugin with the arize-prompt-optimization skill to optimize prompts from traces and experiments via the ax CLI. Try asking your agent:
  • “Optimize this classifier prompt using the last week of eval feedback.”
  • “Revise my support prompt to reduce verbosity while keeping escalation rules.”
Coding agent running the arize-prompt-optimization skill via the ax CLI

Optimize prompts from your coding agent with the arize-prompt-optimization skill

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Next up

When you have versions worth keeping, Save and version prompts in Prompt Hub so your team can track changes, experiment on different versions, and measure improvements over time.