Optimize a prompt

Align prompts with your eval criteria using Prompt Learning - UI, SDK, Alyx, or Skills.

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.

Workflow

By Skills

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.”

By Alyx

1. Describe your goal

   Tell Alyx what to fix, for example “Optimize my prompt to reduce verbosity” or “Rewrite this to improve formatting consistency.”

2. Iterate in chat

   Ask for further adjustments until the result matches what you want.

3. Compare versions

   Review the changes in the Playground and save any version you are happy with to Prompt Hub.

By UI

Navigate to Prompt Hub, find your prompt, and click Optimize.

1. Name task and select dataset

   Enter a task name, then select the training dataset whose rows map to your prompt variables.

2. Select the experiment

   Select the experiment whose evaluation feedback will guide the optimizer.

3. Set the output column

   Choose the column that contains the model output you want the optimizer to improve.

4. Confirm feedback columns

   Confirm feedback columns. Evaluator results are auto-populated. Labels paired with explanations outperform binary labels alone.

5. Select meta-prompt model

   Choose the model that reasons over your examples and feedback to propose prompt updates.

6. Track and ship

   Monitor in Task Logs, compare versions in Prompt Hub, validate in the Playground, then tag the winner as Production.

For diffing and labels, see Save and version prompts.

See compare prompts side by side for the comparison workflow.

By Code

Prompt Learning via SDK

Automate prompt optimization programmatically using the Prompt Learning SDK, which iteratively refines prompts based on evaluation feedback and annotations.

The SDK supports advanced features, like built-in systems to run evaluators, and more flexibility to run multiple loops or define train/test splits.

Learn more about Prompt Learning via SDK →

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.