---
id: prompts
title: Experiment with prompts
kicker: Section 6 of 7
intro:
  heading: Tune, then prove it
  cta: Open Prompts
  scenario:
    label: What you'll do
steps:
  - id: open-prompts
    title: Open Prompts
  - id: open-prompts-tab
    title: Open the Prompts tab
  - id: inspect-prompt
    title: Inspect a prompt
  - id: open-in-playground
    title: Open it in the playground
  - id: duplicate-prompt-in-playground
    title: Duplicate the prompt
  - id: review-playground-prompt
    title: Review the prompt card
  - id: open-variables
    title: Open the variables panel
  - id: review-playground-controls
    title: Review variables and config
  - id: save-prompt-version
    title: Save the prompt
  - id: review-notebook
    title: Review your notebook
  - id: open-create-experiment
    title: Start an experiment
  - id: experiment-info-name
    title: Name the experiment
  - id: experiment-info-versions
    title: Pick prompt versions
  - id: experiment-info-dataset
    title: Choose the dataset
  - id: experiment-info-evaluators
    title: Add the evaluators
  - id: review-experiment-info
    title: Review the experiment setup
  - id: explain-prompt-mapping
    title: Map prompt variables
  - id: complete-prompt-mapping
    title: Review the variable mapping
  - id: explain-eval-mapping
    title: Map evaluator variables
  - id: create-experiment
    title: Create the experiment
---

## intro

A prompt is a collection of messages, variables, and a model — change any of them and you've got a candidate fix. The playground lets you try variations side-by-side, and Experiments run those candidates against your dataset and evals so you can ship with confidence.

## scenario

Inspect the existing prompt, open it in the playground, draft 2–3 variants that fix the citation failure — prompting the agent to tell users its answers come from Wikipedia — then run an experiment against the dataset and evals to see which variant wins.

## step: open-prompts

Click Prompt in the sidebar to see the prompts powering this agent.

## step: open-prompts-tab

Click the **Prompts** tab to see the prompt library for this task.

## step: inspect-prompt

Click the top prompt in the list. Each prompt is a bundle of messages, variables, and a model — change any of those and you've got a new version worth comparing.

## step: open-in-playground

Click **Open in Playground** from the prompt detail view. This creates or reuses a notebook seeded with the prompt so you can iterate without losing the production version.

## step: duplicate-prompt-in-playground

Click **Duplicate Prompt** to copy the seeded prompt into a side-by-side variant. Start from a copy so you can make targeted edits without losing the original baseline.

## step: review-playground-prompt

Use this duplicated prompt card to edit the system prompt so the agent cites its source — e.g. instruct it to tell the user the answer comes from Wikipedia. Edit the messages or try a different model — anything that makes the agent attribute its answers.

## step: open-variables

Click **Variables** to open the panel where you fill in the values your prompts run against.

## step: review-playground-controls

This is the variables panel. The values here control what data your prompts run against — review or fill them in so the experiment compares the same scenario fairly, then close the panel to continue.

## step: save-prompt-version

Click the **Save** icon on this prompt card to store it as a new prompt version. Save at least one prompt before moving on — each saved version is a candidate you can pit against the others in an experiment.

## step: review-notebook

This is your notebook — prompts, variables, and config all live together here so you can iterate side-by-side. Take one last look. When you're ready to prove which variant actually wins, continue to set up an experiment.

## step: open-create-experiment

Open the Experiment menu and choose **Create New**. The tour will stay with you inside the modal while you configure the run.

## step: experiment-info-name

Start by naming the run — a clear name (and an optional description) makes it easy to find later when you're comparing results. Click **Next** when you're done.

## step: experiment-info-versions

Choose the candidate prompt versions you want to pit against each other. These are the saved variants from your notebook — pick the ones worth comparing, then click **Next**.

## step: experiment-info-dataset

Select the dataset and version that holds the captured failure. This is the data every prompt version will run against, so the comparison stays fair. Click **Next** to continue.

## step: experiment-info-evaluators

Add the evals that should judge each result — these decide which variant actually wins. Once your evaluators are in, click **Next**.

## step: review-experiment-info

Here's the full setup for the run — name, prompt versions, dataset, and evals. Give it a once-over, and when it looks right, click **Configure Prompts** to wire up the variables.

## step: explain-prompt-mapping

Variable mappings tell each prompt version where to read its inputs. Map every prompt variable to the dataset column that feeds it — exact name matches are pre-filled as a starting point, but check each one and fill any left empty. The run can't start until every variable is mapped, so don't just click through. Click **Next** when they're all set.

## step: complete-prompt-mapping

That's your prompt-to-data wiring — every variable should now point at a column. Once they're all mapped, click **Configure Evals** to set up the judges (or **Create Experiment** if you skipped evals).

## step: explain-eval-mapping

Each evaluator reads its variables from a source you set. Map the agent's **response** — and any other value the prompt generates — to **Experiment Output** so the eval scores what your new prompt actually produced, not a stored value. Map input variables like the user's question to the **Dataset Column** that holds them; exact name matches are pre-filled. Every variable starts on **Dataset Column**, so switch your response variables to **Experiment Output** before clicking **Next**.

## step: create-experiment

Final review. Double-check every eval variable has the right source — response variables mapped to **Experiment Output** — then click **Create Experiment** to launch the run.
